setwd('/Users/junchaoshen/UPenn BMI')
library(data.table)
library(dendextend)
library(stabs)
library(lars)
library(GGally)
library(mlbench) # for cross validation
library(caret)
library(ggplot2)
library(caret) # LR model training
library(dplyr) # data manipulation
library(ROCR) #for plotting ROC
library(e1071)
library(ggpubr) # for plotting
library(tidyverse) #for data manipulation
library(caret) #for cross validation
library(ROCR) #for ROC
library(pROC) #for generating ROC curves
library(lme4) # for LME model
library(survival) # for survivalan alysis
library(survminer) #for survival analysis
library(ggfortify) # for plotting 
library(gridExtra)
library(grid)
library(sjPlot)
library(sjmisc)
library(nlme)
library(lme4)
library(plyr)
library(knitr)
library(kableExtra)
options(warn = -1)

Background and Introduction

BMIN 503

Overview

Large-scale proteomic analysis identified both diagnostic and prognostic plasma biomarkers for Parkinson’s Disease

Introduction

Background: Parkinson’s Disease (PD) is the second most common neurodegenerative disease affecting over 2 million people in the United States. Patients with Parkinson’s Disease demonstrated a series of cognitive, motor and psychotic symptoms. However, up to date, there still weren’t any cliniaclly validated blood based diagnostic test that can quickly help clinicians to confirm the diagnosis of PD. The fact that different Parkinson’s patients displayed highly heterogenous sypmtoms raised further challenges to the development of such tests. Therefore, the goal of this project is to identify any potential biomarkers associated with the diagnosis and prognosis of PD, and based on that trained a classifier that can 1) differente PD patients from non-PD patients 2) different PD patients with a faster progression rate from slowly progressing PD patients with high performance.

Goal: This project aims to conduct a large-scale proteomic analysis to screen potential plasma proteins that can A) differentiate Parkinson’s patients from normal healthy controls B) differentiate fast cognitive progressors from slow cognitive progressors based on change in MoCA (Montreal Cognitive Assessment) score change over time C) differentiate fast motor progressors from slow motor progressors based on change in UPDRS-III (Unified Parkinson’s Disease Rating Scale) score over time

Faculty and Staff Mentor:

Alice Chen-Plotkin Professor of Neurology

Marijan Posavi Senior Research Scientist

Methods

Data Source: Datasets used for this project were downloaded from NIH Parkinson’s Disease Biomarker Program database. A total of 319 PD + Normal Control Subjects were enrolled in this study through two different cohorts - University of Texas Southwestern School of Medicine and Penn State Hershey School of Medicine. A total of ~1100 plasma proteins were measured in all 319 subjects through a high through-put proteomic platform known as SomaScan. Clinical and demographic information for these patients were also collected at each cohort.

Analysis Plan:

A. Cross Section Analysis

Data Cleaning and Outlier Detection Since those samples were collected from two different cohorts, it would be helpful to start with a principle component analysis (PCA) to detect any potential batch effect. Besides, PCA analysis can help us quickly identify if there’s any significant outliers so that we can remove outliers before formal analysis

Differential Expression Analysis Compare the proteins expression level between normal control and subjects diagnosed with Parkinson’s Disease. Benjamini-Hochberg method was used to adjust for multiple comparison. Proteins that pass the adjusted p-value of 0.05 were considered as proteins that were significantly associated with the phenotype of Parkinson’s Disease. Fold change was calculated using log2(Control-PD). Vocalno plot was used to visualize the difference in protein expression between PD and control group.

3). Gene Set Enrichment Analysis (GSEA)

After identifying plasma proteins that differentiate PD from control, we created a dictionary to map protein names into their corresponding gene symbols. Using KEGG pathway files, GESA analysis was conducted to identify any potential underlying mechanisms associated with the proteins identified in the 2) and their associated genes.

4). Classifcation Analysis

Using the top 3 proteins identified through the differential expression analysis, we next trained a logistic-regression based classification model, validated with 5-fold cross validation over 50 iterations, to understand how well the top 3 proteins we identified - BSP, OMD and ApoM - together with age and sex, can help differentiate PD subjects from control subjects.

  1. Longitudinal Analysis (Cognitive Function)

Linear-mixed effects model was used to evaluate the effect of the top protein biomarkers identified in (A) on change in MoCA score over time, after age and sex were adjusted for.

Model: MoCA ~ time x top proteins + age + sex

  1. Longitudinal Analysis (Motor Function)

Linear-mixed efefcts model was used to evaluate the effect of the top protein biomarekrs indentified in (A) on change in UPDRS-III score over time, after age and sex were adjusted for

Model: UPDRS ~ time x top proteins + age + sex

Results

Differential Expression Analysis ended up identifying a total of 47 proteins that are differentially expressed between PD and normal. With only 8 proteins, the model already start to overfitting as you can see that the model with 8 proteins + age showed an AUC score of 0.78 on training cohort but only less than 0.6 on training cohort. However, it is also possible that there existed a significant batch effect when proteins were measured in samples in two different batches. Clustering analysis clearly showed the presence of co-linearlities between proteins, therefore it might be helpful to use some kind of feature selection algorithms to further reduce the dimensions before training the model. It is very interesting that KEGG pathway enrichment analysis showed that chemokine signaling seemed to be most significantly enriched. However, downstream manupilative experiments are needed to understand the biological meaning of that pathway. It is also very interesting that a lot of proteins showed up in the DE analysis were involved in the inflammatory reaction, like C-reactive protein. This might suggested that PD patients have a higher systemic inflammation level compared to normal control, which is consistent with the wide observation that microglia activation level in higher in the CNS of PD patients than normal control.

Furthermore, generalized linear mixed effect model with random intercept was used to study the effect of top 20 proteins on the long-term clinical outcomes (both on cognitive fucntion and on motor function). Out of the top 20 proteins, FCN1 and BSP seemed to be potential prognostic biomarkers that predict cognitve function. Patients who are in top tertile of BSP measurements developed cognitive function decline much more rapidly compared to patients in the lower two tertiles. Patients who are in the first tertile (the lowest tertile of FCN1 measuremnts) seem to have a faster cognitive function decline than those who are in the first two tertiles. Out of top 20 proteins, IFGBP.2 level seems to be correlated with the incidents of developing dementia according to the Cox Proportional Hazard model. Specifically, patients in the fourth quratile of IGFBP.2 measurements are 7 times more likely to develop dementia compared to those in the lowest quartile.

In addition, longitudinal motor function analysis suggested that NCAM.120 might be a potential biomaker for motor function over time. Patients who have lower NCAM 120 seemed to have a slower motor function progression (not very clear difference though). Also, since it looks like patients with different baseline NCAM 120 measurements have different baseline MoCA score as well, it might be useful for us to include baseline MoCA as a covariant in the LME model as well in the future.

The limit of this study is that this is an entirely analytic project, therefore, no causal relationship can be inferred. A quick screen at the top 20 proteins didn’t identify any significant pQTL effect, which made the Mendelian Randomization (MR) analysis planned in the beginning impossible to conduct. Also, since Somalogic is a new proteomic platform, it remains unknown how reliable the protein measurements are. Therefore, it might be interesting to use ELISA essay to validate some of the proteomic measurements to see how valid they are.

Quick Summary of Replication Cohort

# Read the proteomic dataset
setwd('/Users/junchaoshen/UPenn BMI')
df = read.csv('Protein Demographic.csv',skip = 6)
kable(df[1:5,], "html") %>% kable_styling("striped") %>% scroll_box(width = "100%")
GUID Group Site Age LDOPADoseTm MDS_UPDRS.Total.Score Gender PlateId CHIP CEBPB NSE PIAS4 IL.10.Ra STAT3 IRF1 c.Jun Mcl.1 OAS1 c.Myc SMAD3 SMAD2 IL.23 PDGFRA IL.12 STAT1 STAT6 LRRK2 Osteocalcin IL.5 GPDA IgA LPPL HEMK2 PDXK TLR4 REG4 HSP.27 YKL.40 Alpha.enolase Apo.L1 CD38 CD59 FABPL GDF.11 BTC HIF.1a S100A6 SECTM1 RSPO3 PSP Apoptosis.regulator.Bcl.W VEGF.D SOST FAM3D CSH EFNB1 SNP25 LYPD3 NEGR1 BCL6 FSTL1 Osteopontin Lumican CD177 CHKB SMOC1 protein.Z.inhibitor FLRT2 FLRT3 ISLR2 Vitronectin DSC2 LDLR HXK2 HXK1 SEM5A LTBP4 PIANP Adrenomedullin S100A4 RNF43 TRAIL.R4 ZNRF3 GI24 Ephrin.A2 ApoM IFN.b IFN10 IFNA7 EFNB2 HHLA2 IL.1.sRII AMGO2 RXFP1 C1QR1 NRG4 H2B2E H2A3 H31 IFN.g IL.1F8 IL.1F6 UCRP Ephrin.A3 X14.3.3.protein.beta.alpha X14.3.3E Annexin.V Myostatin C4b Coagulation.Factor.XI CTACK Endostatin TIMP.1 tPA EG.VEGF TIMP.2 TGF.b1 VEGF.sR3 C5 Apo.E BDNF C8 Cathepsin.G CXCL16..soluble FGF.10 FGF.8B GIIE GV MIP.3a SAP SCF.sR TIMP.3 Angiopoietin.4 Cadherin.E GFRa.3 Ephrin.B3 GFRa.2 X6Ckine HMG.1 OPG b.Endorphin Factor.I IGFBP.3 MCP.1 MMP.9 Myeloperoxidase PRL VEGF X4.1BB X4.1BB.ligand Angiopoietin.2 B7 CD30 CLF.1.CLC.Complex Cystatin.C Dtk eIF.5 Ephrin.A4 Ephrin.A5 ERBB2 ERBB3 ERBB4 GA733.1.protein gp130..soluble HO.2 HPV.E7.Type.16 HPV.E7.Type18 HSP.90a.b IL.1.R.AcP IL.10.Rb IL.12.Rb1 IL.13.Ra1 IL.2.sRg Layilin Lymphotoxin.b.R Macrophage.mannose.receptor M.CSF.R MSP.R PAFAH.beta.subunit P.Cadherin PKC.A PKC.Z Rab.GDP.dissociation.inhibitor.beta sICAM.3 suPAR TNF.sR.I TrkC BCMA Bone.proteoglycan.II Calpain.I CK.MM ERBB1 HGF HSP.60 iC3b IGFBP.5 IGFBP.6 MIA NPS.PLA2 OSM PECAM.1 Persephin PF.4 Protein.S TACI TECK Thyroxine.Binding.Globulin TNFSF18 CNTFR.alpha EMAP.2 EPO.R G.CSF.R IL.1F7 Laminin MICA NADPH.P450.Oxidoreductase NANOG NKp44 NovH Siglec.6 Siglec.7 Sonic.Hedgehog IgG TSLP.R ULBP.3 Activin.A Apo.A.I Azurocidin BMP.14 C1q C3 C3adesArg DRR1 FGF.18 FGF.19 FGF.20 FGF9 GDF.11.8 Hemopexin HIV.2.Rev I.309 IGFBP.1 IL.10 IL.16 IL.17F IL.22 Lactoferrin LAG.1 LD78.beta MCP.2 MMP.3 MMP.7 NAP.2 SOD Fibrinogen Apo.B ACE2 Activin.RIB ADAMTS.4 Angiopoietin.1 ART BCAM Cadherin.5 CD97 COMMD7 EDA Fractalkine.CX3CL.1 HAI.1 IL.27 Kallikrein.11 Kallikrein.4 kallikrein.8 Ku70 Lipocalin.2 Met MMP.17 OX40.Ligand sFRP.3 SPINT2 sTie.1 Ubiquitin.1 WIF.1 AIF1 C5a CHK1 ERK.1 Glucocorticoid.receptor Hat1 HDAC8 Karyopherin.a2 MEK1 MOZ PKC.D RAC1 RAD51 TBP Topoisomerase.I UBC9 YES a1.Antichymotrypsin C7 Cardiotrophin.1 CCL28 CD22 HCC.1 IL.4 Midkine MPIF.1 PCNA sRANKL PAI.1 Apo.E3 Apo.E4 Artemin Cytochrome.c Cytochrome.P450.3A4 DAN ER Factor.D Growth.hormone.receptor GX IGFBP.4 IGF.I Luteinizing.hormone Properdin Protein.C PTHrP SCGF.beta VCAM.1 TNFSF15 ALK.1 AREG BMP.7 CD36.ANTIGEN contactin.1 CTGF Desmoglein.1 EDAR ENA.78 ESAM Galectin.4 Gro.a Gro.b.g Histone.H1.2 ICOS IL.1.sRI IL.17.sR IL.18.Rb IL.1Rrp2 JAM.B JAM.C LSAMP MBL NKp30 PD.L2 PTP.1B Siglec.9 TGF.b.R.III TSLP CTLA.4 a2.Antiplasmin bFGF Calpastatin Ck.b.8.1 DC.SIGN DC.SIGNR FSH Galectin.2 GFAP IL.19 IL.1b I.TAC MIP.1a MRC2 Myoglobin ON PARC PTN resistin Trypsin vWF Fas.ligand..soluble Flt3.ligand Haptoglobin..Mixed.Type IL.4.sR NKG2D WISP.1 BAFF C9 Cathepsin.B FGF.5 Galectin.3 GDF.9 IgM IL.2 IL.13 IL.18.BPa LBP Coagulation.Factor.Xa PlGF TIG2 ULBP.1 ULBP.2 XEDAR Aurora.kinase.A MK01 SMAC VEGF.C sCD4 IL.2.sRa TNF.sR.II Siglec.3 ADAMTS.5 IDUA AMPM2 amyloid.precursor.protein ARSB ASAHL ATS1 ATS13 Carbonic.Anhydrase.IV CATC Cathepsin.A Cathepsin.S CD39 Coagulation.Factor.VII C2 CRIS3 Enterokinase WFKN1 Glutamate.carboxypeptidase GPVI Granulysin HPLN1 IDE IDS kallikrein.12 kallikrein.13 kallikrein.5 KREM2 LKHA4 LYVE1 MATN3 MEPE METAP1 ASAH2 Nidogen PIGR Protease.nexin.I RET SARP.2 Semaphorin.3A TrATPase URB WFKN2 Aggrecan ANGL3 BGH3 BGN C1r Carbonic.Anhydrase.X CD109 CD23 CD48 CD5L CFC1 CNTN2 Contactin.4 Contactin.5 CYTF Cystatin.M DLL4 FCG2A FCG2B FCG3B FCGR1 FCN2 GFRa.1 GPC2 Heparin.cofactor.II HTRA2 IGFBP.7 IL24 LRIG3 LRP8 LY9 MATN2 Nectin.like.protein.2 NET4 PGRP.S RGMB RGM.C TFPI TSP2 TSP4 ABL1 Aminoacylase.1 Antithrombin.III AURKB BARK1 BMP.1 CAMK2A CAMK2B Carbonic.anhydrase.6 Carbonic.anhydrase.VII CDK2.cyclin.A CDK5.p35 CDK8.cyclin.C Chk2 CLC4K CRDL1 CSK Cathepsin.V Dkk.4 ECM1 FETUB Granzyme.H HCK IL.17.RD Kallikrein.7 KPCI LYNB PAK3 PAK7 PCI PIK3CA.PIK3R1 PK3CG PKB.a.b.g PLK.1 Renin SHP.2 STAB2 TBK1 TCPTP TPSB2 TPSG1 UFC1 Bcl.2 BFL1 BMX BSP BTK CAMK1D CAMK2D Carbonic.anhydrase.XIII CD30.Ligand CDK1.cyclin.B Chymase CSK21 EphA1 EPHA3 FN1.3 FN1.4 Flt.3 FSTL3 granzyme.A GSK.3.alpha.beta HIPK3 IL.15.Ra IL.18.Ra IL.8 IR Kallistatin Kallikrein.6 LCK LYN Periostin PDGF.Rb PGCB PRKACA RPS6KA3 sE.Selectin STK16 Survivin Thrombopoietin.Receptor Thrombospondin.1 TrkA TRY3 DUS3 XPNPEP1 Angiotensinogen b2.Microglobulin b.ECGF BLC Catalase CNTF FGF.17 GCP.2 IFN.aA IL.17B Integrin.a1b1 LEAP.1 Lymphotoxin.a1.b2 Lymphotoxin.a2.b1 MDC MIP.5 Proteinase.3 SDF.1 TAFI TARC TGF.b3 TSH Vasoactive.Intestinal.Peptide CD40.ligand..soluble DKK1 dopa.decarboxylase Adiponectin a1.Antitrypsin a2.HS.Glycoprotein Arylsulfatase.A BASI BMP10 Cadherin.6 CAMK1 Caspase.3 CATE Chitotriosidase.1 CHL1 CLC7A MASP3 Discoidin.domain.receptor.2 DKK3 DPP2 Endothelin.converting.enzyme.1 EphB4 FCN1 GNS HGFA IL22RA1 LGMN LY86 Marapsin MMEL2 MP2K2 MRCKB Nectin.like.protein.1 NID2 OBCAM OCAD1 OLR1 RAP SLAF5 SLIK1 Soggy.1 TEC TLR4.MD.2.complex VEGF.sR2 BMPER Cadherin.12 Calcineurin.B.a complement.factor.H.related.5 IGF.II.receptor kallikrein.14 Macrophage.scavenger.receptor MFRP Albumin a2.Macroglobulin ALT Angiostatin CK.MB p27Kip1 BNP.32 PYY Secretin TNR4 CSF.1 Activated.Protein.C COX.2 STX1a sTie.2 ADAM.9 ANGL4 Cadherin.2 Carbonic.anhydrase.9 Carbonic.anhydrase.III CK.BB Cystatin.S CYTD Endocan EphA5 FGF23 FGFR.2 FGFR.3 FGR FYN IL.12.RB2 KPCT MAPK2 MAPK5 MAPKAPK3 MATK MK08 PAK6 PDGF.CC pTEN PTK6 TLR2 UFM1 AIP Cyclophilin.A DLRB1 ETHE1 GAPDH..liver HSP.40 MDHC NACA Peroxiredoxin.1 PPAC PSA1 PSA6 RS7 RSK.like.protein.kinase SBDS SE6L2 SGTA TCTP TMA UB2L3 ARI3A CaMKK.alpha CDC37 DLC8 IMB1 ING1 Lamin.B1 LDH.H.1 MBD4 Mesothelin NAGK Phosphoglycerate.mutase.1 PLPP PSD7 SKP1 Sorting.nexin.4 UBE2N discoidin.domain.receptor.1 FGF.4 HSP.70 sRAGE BPI C6 Eotaxin.2 Factor.B FGF.6 Fibronectin FST Granzyme.B HB.EGF IgE IL.17D IL.17E IL.20 IL.6.sRa IL.7 IP.10 Lymphotactin MCP.4 Neurotrophin.3 Neurotrophin.5 PAPP.A PDGF.BB Plasmin Plasminogen Prekallikrein alpha.1.antichymotrypsin.complex P.Selectin Tenascin TGF.b2 Thrombin uPA Factor.H MMP.2 Transferrin Histone.H2A.z Thyroglobulin X14.3.3 X4EBP2 X6.Phosphogluconate.dehydrogenase Aflatoxin.B1.aldehyde.reductase AK1A1 AN32B Cofilin.1 DRG.1 EP15R ERAB FER HNRPQ IF4G2 IGF.I.sR IL.1.R4 LCMT1 LIN7B M2.PK MDM2 NCAM.L1 NDP.kinase.B NSF1C NUDC3 PA2G4 paraoxonase.1 PESC PFD5 PHI prostatic.binding.protein Protein.disulfide.isomerase PSA2 RAN RBM39 SNAA Sphingosine.kinase.1 Spondin.1 Thymidine.kinase transcription.factor.MLR1..isoform.CRA_b Transketolase Triosephosphate.isomerase XTP3A PTP.1C AMNLS CYTT BOC CLC1B SAA CRP sICAM.1 DAPK2 DYRK3 MIC.1 DHH FGF.12 FGF.16 FGF.8A IFN.lambda.1 IFN.lambda.2 MSP SLPI SP.D ADAM12 BCL2.like.1.protein CHST2 CHST6 Collectin.Kidney.1 ENPP7 ENTP3 ENTP5 FCRL3 GREM1 hnRNP.A.B LRRT1 LRRT3 MFGM PCSK7 PDPK1 Sialoadhesin SPARCL1 SPHK2 ST4S6 TGM3 Tropomyosin.2 Ubiquitin ZAP70 C1.Esterase.Inhibitor C3b C4 C5b..6.Complex FGF7 IL.3.Ra IL.5.Ra IL.11 Kininogen..HMW MMP.12 NCAM.120 PDGF.AA SCGF.alpha ATS15 BSSP4 BST1 CBX5 CDON Clusterin CONA1 CTAP.III DnaJ.homolog EMR2 FLRT1 Fucosyltransferase.3 FUT5 GP114 HDGR2 IL.34 KIRR3 KYNU Livin.B NXPH1 PLCG1 PLXC1 RSPO2 SH21A SLIK5 SORC2 PH IL.6 X3HIDH GM.CSF TNF.b X41 X17.beta.HSD.1 Apo.D IL.3 PPIB Protein.disulfide.isomerase.A3 TFF3 Afamin Olfactomedin.4 ASM3A FAM107B Gelsolin CBG Cytidylate.kinase C34.gp41.HIV.Fragment PERL CO8A1 ITI.heavy.chain.H4 TXD12 STRATIFIN sL.Selectin TRAIL.R1 Epithelial.cell.kinase G.CSF Glypican.3 IL.1a BMPR1A BMP.RII TrkB VEGF121 Angiogenin Coagulation.Factor.IX Coagulation.Factor.X GDF2 Insulin MCP.3 WNT7A ACTH Glucagon C3a Calcineurin Caspase.2 Coactosin.like.protein Coagulation.Factor.V D.dimer Endoglin Galectin.8 GIB Glutathione.S.transferase.Pi GOT1 HCC.4 HCG Hemoglobin IgD Integrin.aVb5 Lysozyme MIP.3b MIS MMP.1 MMP.13 SHBG Stanniocalcin.1 TF EPI X40S.ribosomal.protein.SA AGR2 annexin.I annexin.II ARMEL ARP19 ARTS1 ATP.synthase.beta.chain C1QBP CAPG Carbonic.anhydrase.I carbonic.anhydrase.II CATZ cIAP.2 CRK DBNL DERM DSC3 Elafin ERP29 Esterase.D FABPE FAK1 FCAR FGFR4 Fibrinogen.g.chain.dimer GP1BA GPC5 GRN GSTA3 hnRNP.K HPG. HRG IF4A3 JAK2 LG3BP Mammaglobin.2 MMP.14 MK11 MK12 MK13 MAPK14 Mn.SOD Moesin PBEF Myokinase..human NCC27 NCK1 PAFAH PARK7 Peroxiredoxin.5 Peroxiredoxin.6 PGP9.5 phosphoglycerate.kinase.1 PPase PUR8 Rb RS3 sCD163 SEPR SIRT2 SPTA2 SSRP1 Tropomyosin.1.alpha.chain Trypsin.2 TS TSG.6 B7.H1 B7.H2 CD226 CD244 CD83 CLM6 CRTAM DAF DcR3 EPHAA EPHB2 EphB6 GPNMB IL.1.sR9 IL.17B.R IL.20.Ra IL.22BP IL.23.R IL.7.Ra ILT.2 ILT.4 JAG1 JAG2 JAML1 KI2L4 KI3L2 KI3S1 KLRF1 LAG.3 LIMP.II MICB MO2R1 NKp46 Nogo.Receptor NOTC2 Notch.1 Notch.3 Nr.CAM NRX1B NRX3B OX2G Prolactin.Receptor RELT ROBO2 ROBO3 SEM6B Semaphorin.6A sICAM.5 SIG14 SLAF6 SREC.I SREC.II TAJ TCCR TGF.b.R.II TIMD3 TWEAKR UNC5H3 UNC5H4 PDE7A AMPK.a1b1g1 K.ras NMT1 PDE9A PPID PSME3 GCKR CK2.A1.B CK2.A2.B PDK1 KIF23 IMDH1 HMGR PCSK9 NR1D1 PPIE MP2K4 JNK2 AMPK.a2b2g1 cGMP.stimulated.PDE Cyclophilin.F DRAK2 IMDH2 PDE11 PDE1A PDE3A PDE4D PDE5A TAK1.TAB1 TYK2 ABL2 BCAR3 calreticulin GRB2.related.adapter.protein.2 MMP.16 RAC3 SHC1 VAV GHC2 Eotaxin Coagulation.Factor.IXab Elastase Apo.E2 Troponin.T Prothrombin EGFRvIII annexin.VI B7.2 calgranulin.B Caspase.10 CBPE CKAP2 CPNE1 Cyclin.B1 DLL1 GPC6 hnRNP.A2.B1 HVEM IL.1Ra Keratin.18 LIGHT MIF NLGNX OMD PIM1 PKB.beta Semaphorin.3E SET BAFF.Receptor BRF.1 Fas..soluble sLeptin.R DR6 CAD15 CD27 RANK SHPS1 FABP Troponin.I..skeletal..fast.twitch Troponin.I ANP ALCAM ASGR1 CNDP1 COLEC12 CYTN DEAD.box.protein.19B Ficolin.3 GAS1 GRB2.adapter.protein H6ST1 HSP.90b IL.17.RC MED.1 PKC.B.II PKC.G PSMA RANTES RASA1 RBP RGMA RS3A sICAM.2 SLAF7 SRCN1 Stress.induced.phosphoprotein.1 Testican.1 Testican.2 WNK3 RUXF Cathepsin.D EGF GITR bFGF.R TRAIL.R2 NRP1 AFP BID b.NGF C3d CD70 Cripto Epo GDNF IFN.g.R1 IL.9 LIF.sR NG36 Noggin S100A12 tau X14.3.3.protein.zeta.delta X3HAO aldolase.A ARGI1 BAD Dynactin.subunit.2 EF.1.beta eIF.4H eIF.5A.1 Gastrin.releasing.peptide HINT1 HSP70.protein.8 Nucleoside.diphosphate.kinase.A PEX5 PSME1 S100A7 WISP.3 Ferritin TNF.a TWEAK Tpo PTH Somatostatin.28 MP2K3 p53 PolyUbiquitin.K48 PolyUbiquitin.K63 NET1 CD47 ANK2 X14.3.3.protein.theta CREL1 Lectin..mannose.binding.2 LRP1B MYPC1 N.terminal.pro.BNP Tropomyosin.4 PACAP.27 ghrelin PACAP.38 a.Synuclein BMP.6 HGH SOD3 RSPO4 Cathepsin.H Activin.AB PSA IGFBP.2 RNase.H1 ROR1 CgA MMP.10 FBLN3 Leptin KEAP1 gpIIbIIIa Eotaxin.3 SUMO3 IL.17 CSRP3 MMP.8 PGM1 DSCAM MUC1 FAM3B NEUREGULIN.1 INGR2 sLRP1 IFN.a.b.R1 TFF1 HMGN1 MIG CD63 Trefoil.factor.2 Galectin.7 LEG9 UB2G2 Transgelin.2 ATPO Corticotropin.lipotropin QORL1 PEDF CATF FTCD UBP25 PLXB2
PDJA900DTV PD UTSW 75.4 900 59 Male Set.2 622.8 777.0 13270.8 758.2 1566.3 535.7 1099.4 3278.6 1213.1 444.0 396.5 5958.3 4141.0 1975.0 1374.4 270.4 3393.6 262.7 1799.5 13617.1 441.9 1928.4 3838.0 674.2 1335.4 263.5 358.8 2881.7 2385.7 9108.6 12010.7 34379.0 720.8 3455.3 9422.4 968.5 854.9 495.3 1049.5 1857.9 1859.7 789.6 530.0 801.4 61475.6 44835.6 1962.8 2262.4 1672.6 996.2 6736.5 1626.5 44037.0 4983.9 1556.7 1158.0 1987.6 5300.6 5374.3 30057.5 3873.9 7025.3 81392.8 5210.4 175.9 463.5 1138.8 6108.5 2070.5 7668.0 1766.8 1866.3 1067.0 1170.6 652.4 507.3 2121.9 17702.8 600.5 351.4 321.4 19299.7 886.9 14621.2 2314.6 435.8 15967.2 1034.0 11992.3 599.2 1559.2 1368.6 482.6 639.1 2733.4 2465.7 6046.2 752.2 812.5 7784.8 517.1 1404.8 3156.4 106204.8 99.4 679.5 541.9 438.4 1366.0 9119.1 7429.0 53512.9 833.9 3623.3 896.0 15844.8 729.2 712.7 302.7 407.7 167.6 43679.1 21319.3 987.2 1287.8 74597.2 956.2 1354.4 6978.2 26884.9 3535.9 14268.0 827.4 40698.9 1807.7 1105.2 6138.8 18582.5 2323.7 19097.5 454.5 884.1 178.9 840.5 1124.7 519.8 4837.1 588.4 908.0 3398.8 5800.2 455.0 341.7 203.2 564.9 7718.2 1030.7 488.5 1992.5 2888.1 22621.2 1263.4 546.0 3640.7 1769.9 2175.9 6109.2 10453.8 198.9 521.9 3298.7 31645.8 10387.3 668.4 7144.0 782.3 12260.0 2646.9 5915.3 11910.0 3367.0 18937.7 1103.4 26720.0 1435.9 3224.9 8723.2 2636.4 1337.2 4698.1 4239.2 1921.5 1004.0 551.6 27153.0 5597.1 1030.3 1526.9 41172.1 600.7 14404.9 3171.4 319.5 913.7 2041.3 2268.9 721.8 2530.0 230.5 356.8 590.3 5072.0 1700.9 762.4 199622.0 1014.0 494.2 4331.5 18790.8 258.1 2493.0 47113.7 60507.9 169741.8 471.6 351.0 29909.2 357.0 614.6 1215.3 1170.8 236.4 3234.0 1282.3 233.9 312.4 1109.0 1094.9 39463.4 414.8 1849.1 265.5 661.7 2196.9 6797.1 701.9 154812.8 13147.8 507.9 285.5 218.9 661.6 3649.0 2107.4 15123.2 1697.2 2934.3 1648.5 5296.0 7533.9 939.4 1114.5 421.3 3544.0 241.3 31238.4 9418.3 1244.3 326.0 4592.5 1310.7 11528.8 1292.8 3959.6 2074.4 13162.7 1709.1 9941.4 88.9 365.6 1600.5 296.3 389.3 2240.9 3241.1 6666.9 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PDMH749VDY PD UTSW 66.8 15 50 Male Set.4 921.9 852.0 30894.8 693.5 1429.2 1527.1 1139.3 3838.6 1882.2 301.3 329.7 12523.5 9029.6 1119.1 2022.5 190.2 13219.1 404.2 2181.1 1058.1 239.5 2066.5 919.8 430.3 2281.4 321.0 242.7 1490.9 9001.1 5842.0 39645.7 31187.1 740.3 2003.0 5482.4 897.8 417.3 369.2 1518.3 1172.0 1294.5 257.2 522.1 456.7 29765.2 8790.3 2011.4 1306.5 1282.6 1215.9 3912.3 1479.7 31896.3 11034.5 1093.8 887.8 2666.4 3460.5 7345.6 18393.0 5298.5 5586.0 37756.5 2545.3 142.7 455.3 1250.3 9892.8 1722.1 3820.7 1794.3 1508.9 1018.6 898.0 398.6 390.6 994.1 20008.9 477.7 291.3 186.4 13262.8 955.2 9795.3 2821.2 334.4 8850.3 907.5 12152.4 605.6 1161.9 734.8 430.8 709.8 1850.7 1989.9 15305.5 896.3 825.0 4282.3 79.7 860.9 1034.1 49830.1 48.4 681.0 167.0 311.9 1163.3 7723.4 8325.1 56980.8 861.1 3107.1 700.7 12643.7 211.7 769.4 241.1 239.2 116.8 41600.3 10743.8 1404.6 483.9 64320.1 734.4 767.1 4600.9 25291.4 4610.4 17710.7 1257.4 42869.4 1856.2 779.3 6589.4 15148.7 909.6 9985.5 281.6 340.5 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2904.7 470.9 455.7 1176.3 912.9 299167.3 1666.1 663.8 5696.5 960.2 5195.8 443.4 17943.8 3025.2 297.3 731.2 562.8 180334.0 207934.3 492.5 2550.8 11554.0 788.4 722.7 1071.1 1360.1 630.2 5455.3 1040.0 502.3 111842.4 1810.8 730.6 959.1 13105.8 697.1 1394.7 1447.9 2865.3 8006.1 382.7 1300.1 2538.3 837.6 480.5 3445.8 509.2 2625.2 390.5 773.1 573.5 12404.1 1070.0 302.8 731.5 8433.3 2810.5 1731.7 9053.8 495.8 4531.6 2468.7 20070.7 29858.8 281.3 98.6 2199.6 1278.4 7535.9 75282.8 1210.9 52735.7 352.0 1537.1 491.0 8564.5 2719.9 2391.9 661.4 4000.1 4413.2 10320.5 3501.3 1657.3 1262.1 12519.2 8376.0 480.8 398.5 24823.9 325.8 462.2 6539.6 1290.3 69252.6 3043.2 552.5 16453.5 483.7 2505.2 1614.2 644.7 12029.5 96411.1 5949.7 329.4 2444.4 320.4 421.2 750.0 1843.6 4989.4 4734.2 935.8 295.4 1174.6 10437.6 5633.9 431.9 1429.4 20068.9 15033.0 1067.4 9273.3 339.9 3022.8 2242.0 734.0 5486.6 900.3 1599.8 716.1 2868.7 258.5 212.3 1477.1 1150.1 6399.9 2133.0 1285.1 1654.5 3701.5 6763.7 1240.5 152.4 2134.7 706.8 280.3 507.7 222.4 2008.2 6347.2 6500.9 1535.3 1120.4 1851.2 5212.7 1911.1 9472.8 2451.9 7514.2 389.2 371.8 48674.3 2243.9 3327.5 732.6 4782.1 2961.7 727.8 1528.1 1860.5 1427.3 5442.9 735.0 625.4 10748.2 394.6 48.3 753.4 1921.1 1705.3 1180.7 968.0 321.6 3607.0 5144.1 55420.4 213.9 8297.0 2366.8 2496.6 9324.7 2365.6 905.4 386.1 2530.9 1143.8 62041.8 10683.6 527.8 140.4 4477.8 147437.8 819.4 5628.1 3683.2 900.9 2560.5 10404.9 358.2 321.7 1636.2 1299.1 191.7 219.3 2856.0 4945.6 1558.4 7888.3 15720.1 5315.4 226.0 174.6 528.2 1731.2 1320.7 38511.7 1198.8 638.8 103542.9 618.8 2126.1 3921.2 182.5 981.7 4375.7 822.3 226.8 513.9 2654.1 236.7 14037.0 971.5 1580.2 269.3 2896.8 5305.9 2855.7 7499.7 1219.9 2327.0 709.6 510.9 992.6 8311.6 134.8 1334.5 51634.2 2620.0 6517.4 3188.7 9738.1 633.9 802.0 9546.1 944.8 7646.5 41367.4 2724.1 290.8 5767.9 5411.3 7775.2 2524.2 1180.8 612.2 20099.8 4758.5 604.1 183.2 1542.6 3533.5 445.3 1905.7 3722.2 4849.3 1734.5 196.8 3336.7 9167.7 268.2 205.7 1368.3 165.8 151.1 1253.1 13153.0 210.7 2226.4 1185.8 4259.3 6223.6 5509.9 15218.8 1012.2 451.6 580.5 241.5 224.5 15481.2 421.9 4084.5 1049.5 1877.0 2348.4 414.1 1453.8 577.2 6463.1 6012.3 408.1 2333.9 13716.8 589.1 5465.7 1441.8 6437.5 1692.0 20716.6 2635.2 2089.0 2252.0 4362.7 2914.9 2613.5 4311.4 7545.0 965.1 1734.2 1259.1 1306.6 2556.9 2906.5 921.1 1179.9 796.8 6104.2 1292.1 2856.4 3104.5 4289.1 6808.2 1014.8 589.1 7231.5 1151.3 40430.7 8702.2 863.4 1266.3 10171.6 10263.0 2900.7 19742.3 490.3 1288.7 355.1 2352.7 214.6 528.6 1373.5 642.7 394.8 295.2 2175.7 2058.6 4925.6 1512.7 1771.3 3072.9 1728.1 141.6 14829.9 1319.2 681.9 929.7 230.2 1879.6 381.2 7324.7 862.3 3344.7 6067.2 9908.5 923.5 310.6 1006.9 5588.1 351.1 932.8 197.3 461.0 2637.6 5683.5 76628.8 1650.6 9091.0 60915.0 547.0 17348.8 4451.4 1082.3 3680.9 1157.4 518.2 743.5 146.3 8857.6 750.5 1914.3 8397.4 2150.7 923.7 378.8 243.7 352.0 301.1 4508.7 14256.7 541.0 1057.3 366.6 346.3 1593.4 11472.7 1748.9 267.0 3088.8 12543.6 2698.1 344.4 289.5 8746.5 363.6 350.9 94280.0 305.7 24896.5 253.8 5881.0 1759.1 197.5 262.2 947.9 199.6 389.0 341.6 15300.6 191.4 1718.0 531.6 457.9 190.7 201.2 16820.6 18008.1 702.0 3671.7 25360.8 7839.9 10992.2 29138.2 214.5 1343.4 2371.2 53468.6 11126.1 250305.5 2319.2 327.0 2696.0 206.6 8041.3 1738.7 2741.4 176.7 7072.4 17050.9 482.4 1742.8 2609.5 2389.6 2187.0 8711.9 6350.8 1504.3 382.1 25652.1 202.3 511.3 12957.3 347.5 261.5 977.2 167.3 199.9 483.4 15083.0 3498.7 229.0 547.7 3015.0 286.4 4195.1 6907.0 2173.3 525.4 223.8 27708.7 4822.3 14127.8 3482.5 169.2 24381.9 1289.0 11957.8 258.1 13876.0 3817.9 2130.0 2269.1 1289.2 353.8 227.0 1188.9 257.4 243.8 162.1 7831.0 36717.5 2366.1 431.3 1184.3 429.1 302.2 20286.0 11752.2 1174.1 6418.3 361.4 373.9 1103.0 111.6 203.2 1072.5 2606.1 1040.9 140.0 17298.0 550.9 4900.4 161.7 2591.7 949.6 16853.7 10372.6 10518.6 108185.7 515.3 1224.2 725.9 2578.1 294.4 21348.8 1058.3 20412.9 6975.5 2099.1 316.1 1808.3 13195.0 430.9 7136.0 1264.1 4462.3 13754.0 550.8 1594.7 672.5 324.6 11948.0 745.1 665.9 1000.4 270.1 857.5 105.4 6647.0 519.3 806.7 308.7 5305.2 2396.0 375.4 2455.7 369.4 595.9 250.9 936.6 3234.5 1668.3 5455.9 1732.7 3692.2 3130.7 10517.5 41770.3 172.2 3715.5 2272.9 882.4 608.6 5698.0 940.6 622.0 1080.6 37009.1 2720.1 677.3 5458.6 1576.8 10246.7 2127.6 1209.0 805.3 1076.8 467.0 1388.2 508.3 4540.3 12477.9 6362.0 3007.0 269.2 481.7 291.9 797.9 4023.6 649.9 1007.4 449.6 2694.1 72478.7 13368.5 1541.2 468.6 855.0 4150.4 36907.7 30452.5 614.2 753.6 2513.0 819.6 135225.5 432.7 14669.7 770.2 589.8 2569.2 1733.0 1787.4 918.0 2346.1 1123.5 5560.3 5955.4 4720.7 1746.3 126924.2 1689.3 5539.0 870.7 5890.5 182.5 5176.3 168.5 4577.3 3145.1 4757.1 311.1 15587.3 5113.6 2495.5 794.8 347.2 303.1 2226.2 61663.0 1596.6 717.9 28930.6 410.4 245.2 1165.3 6003.9 394.3 159140.4 1032.5 8980.6 3555.2 631.6 727.0 600.2 1903.9 59557.5 1096.7 458.3 11777.5 3478.2 1194.9 867.6 1057.4 464.4 453.2 1366.0 2228.3 17654.8 1142.0 1848.6 422.6 2514.0 968.2 7376.5 188.2 456.1 1007.0 4546.8 361.4 1906.5 816.1 1061.5 816.8 131.5 390.3 3754.7 559.0 17364.9 1859.1 400.1 15782.8 1563.2 4921.9 1145.4 1562.6 262.2 2433.4 3523.3 309.5 4307.1 9304.0 671.1 287.1 432.2 822.1 819.6 369.1 573.5 8617.2 367.0 866.7 1050.5 2856.0 2147.9 501.9 27976.2 1025.0 22859.1 1881.0 1117.8 2541.7 1469.3 2642.0 3855.8 434.8 3709.2 14831.0 1018.1 24557.5 406.7 7091.8 496.3 810.5 1564.4 1005.2 7771.8 152.9 3882.9 6581.1 978.2 323.7 1242.6 1969.3 585.1 8483.5 738.7 757.3 4431.9 564.9 1890.6 615.0 1139.7 353.5 660.8 3472.7 677.0 50224.5 1129.4 3774.1 5334.8 9186.5 4058.4 2017.9 2669.2 939.1 2211.1 1261.7 12178.0 2325.4 1465.9 1720.2 1496.2 2505.3 442.3 780.8 6428.8 10135.6 10464.7 1101.3 10801.6 8969.7 382.7 265036.0 4446.1 189670.5 181.4 2861.5 1145.7 3248.7 3393.2 2954.0 17181.3 2021.9 386.1 2167.9 1566.6 1886.0 619.8 3223.4 803.8 665.2 778.0 688.2 10977.1 722.9 9408.6 5200.6 610.4 2670.4 1826.8 1617.7 1367.8 8236.5 4517.7 7604.5 1097.9 10922.3 15271.4 9862.6 574.8 713.6 17356.7 1056.6 9446.3 3481.3 1467.7 2932.1 467.0 919.1 25312.2 2726.8 33141.2 1341.1 99060.1 5162.6 498.5 280.8 18728.4 1037.6 261.2 23626.9 590.9 1570.7 73288.5 49618.6 5028.2 2276.0 1120.4 756.5 2845.2 717.8 806.8 888.0 925.9 341.7 3227.2 834.4 1277.7 649.5 9556.1 243.7 2652.7 905.8 345.2 477.8 176.1 4963.6 747.2 2240.9 653.8 198.1 125876.8 5636.7 71704.2 890.3 5332.0 1256.9 516.6 2922.8 21107.1 324.1 1125.7 1614.3 538.6 202.1 1145.1 262.6 2654.5 37750.6 470.3 698.0 159.1 2993.8 1749.3 1921.8 1010.3 1158.9 3796.4 489.2 557.4 490.1 1055.7 2387.4 15029.2 26496.8 3119.1 4920.6 64432.2 332.8 988.1 256.7 5973.7 26731.5 221.0 941.2 313.1 1332.2 741.2 5543.7 768.3 169.6 598.4 6438.5 741.4 1248.5 2742.5 452.3 1089.5 801.3 2160.3 355.9 3621.8 1118.4 649.5 1226.7 1660.0 1315.5 181.0 400.1 1850.7 1776.9 2972.3 702.9 478.9 369.0 10087.9 579.3 1637.2 3761.6 3774.6 3493.2 811.7 1302.9 52761.7 2777.9 8551.4 1169.2 5494.1
# Let's extract the columns that include proteine expression only
protein = df[9:1313]
# Let's first normaliz the dataset using log10 transformation
protein.log = log10(protein)
# Let's first do some quality control analysis to identify potential outliers
dat.pca <- prcomp(protein.log,cor=F)
protein.log$Site = df$Site
autoplot(dat.pca)

# Based on the PCA plot,looks like there's one significant outlier. let's remove that outlier before further analysis
protein.log = protein.log[-309,]
df = df[-309,]
#Now, we can add clinical variables back to the pre-processed protein values
protein.log$GUID = df$GUID
protein.log$Group = df$Group
protein.log$Site = df$Site
protein.log$Age = df$Age
protein.log$LDOPA = df$LDOPADoseTm
protein.log$UPDRS = df$UPDRS

Differential Expression Analysis

Now, let’s move on to compare the proteomic expression level difference betwen normal control and Parkinson’s patients

setwd('/Users/junchaoshen/UPenn BMI')
library(GEOquery)
library(oligo)
library(limma)
library(viridis)
library(GEOquery)


design <- model.matrix(~ -1 + factor(protein.log$Group))
colnames(design) <- levels(factor(protein.log$Group))
fit <- lmFit(t(protein.log[,1:1305]), design)
fit.contrast <- makeContrasts(diff = Control - PD, 
                                  levels = design)
fit2 <- contrasts.fit(fit, fit.contrast)
fit2 <- eBayes(fit2)
exposure_results <- topTable(fit2, coef = "diff", adjust = "BH", num = Inf)
exposure_results[exposure_results$sig == 'significant',]
## [1] logFC     AveExpr   t         P.Value   adj.P.Val B        
## <0 rows> (or 0-length row.names)
library(ggplot2)
library(ggrepel)
# Assign (in)significant genes
exposure_results$sig <- rep("insignificant", nrow(exposure_results))
exposure_results$sig[which(exposure_results$adj.P.Val<0.05)] <- "significant"
v1 = ggplot(exposure_results, aes(x = logFC, y = -log10(adj.P.Val), color = sig)) + 
  geom_point() +
  theme_bw() +
  ggtitle("Volcano plot") +
  xlab("LogFC")+
  ylab("-Log10(q-value)") +
  scale_color_manual(values = c("black", "red")) +
  theme(legend.position = "none")

v1+geom_text_repel(data=head(exposure_results, 40), aes(label=rownames(exposure_results)[1:40]))

dict = read.csv('Protein Demographic.csv',skip = 1)

dict1 = t(dict[4:5,])[-1:-8,]
rownames(dict1) = NULL
colnames(dict1) = c('SYMBOL','Protein.Name')
exposure_results$Protein.Name = rownames(exposure_results)
protein_gene = merge(exposure_results,dict1,by = 'Protein.Name')
DE_res = protein_gene
write.table(exposure_results, "./Protein_DE_results.txt", row.names = F, 
            col.names = T, quote = F, sep = "\t")

df_boxplot1 = data.frame(
  expression = protein.log[,'BSP'],
  status = protein.log$Group)

ggplot(df_boxplot1, aes(x = status, y = expression)) +
  geom_boxplot(outlier.colour = NA, color = "grey18", fill = "lightblue") +
  stat_boxplot(geom = "errorbar", color = "grey18") +
  geom_jitter(size = 1, position = position_jitter(width = 0.3)) +
  ggtitle("BSP Expression Level between PD and Control") +
  xlab(" ") +
  ylab("BSP Expression Level (RFU)") +
  theme_bw() +
  theme(legend.position = "none")

df_boxplot2 = data.frame(
  expression = protein.log[,'OMD'],
  status = protein.log$Group)

ggplot(df_boxplot2, aes(x = status, y = expression)) +
  geom_boxplot(outlier.colour = NA, color = "grey18", fill = "lightblue") +
  stat_boxplot(geom = "errorbar", color = "grey18") +
  geom_jitter(size = 1, position = position_jitter(width = 0.3)) +
  ggtitle("OMD Expression Level between PD and Control") +
  xlab(" ") +
  ylab("OMD Expression Level (RFU)") +
  theme_bw() +
  theme(legend.position = "none")

df_boxplot3 = data.frame(
  expression = protein.log[,'ApoM'],
  status = protein.log$Group)

ggplot(df_boxplot3, aes(x = status, y = expression)) +
  geom_boxplot(outlier.colour = NA, color = "grey18", fill = "lightblue") +
  stat_boxplot(geom = "errorbar", color = "grey18") +
  geom_jitter(size = 1, position = position_jitter(width = 0.3)) +
  ggtitle("ApoM Expression Level between PD and Control") +
  xlab(" ") +
  ylab("ApoM Expression Level (RFU)") +
  theme_bw() +
  theme(legend.position = "none")

library(gplots)
# keep top 200 genes
top_results <- subset(exposure_results,sig =='significant')
top.eset <- protein.log[colnames(protein.log) %in% row.names(top_results)]
status.colors <- unlist(lapply(protein.log$Group, 
                               function (x) {if (x == "PD") "red"  
                                 else "navy"}))
heatmap.2(t(top.eset), col = viridis(256, option = "B"),
          trace = "none", keysize = 1.5, key.title = NA,
          ColSideColors = status.colors)
legend("topright", legend = c("PD","Control"), fill = c("red", "navy")) 

library(fgsea)
kegg <- gmtPathways("./c2.cp.kegg.v7.4.symbols.gmt")
reactome <- gmtPathways("./c2.cp.reactome.v7.4.symbols.gmt")
pathways <- c(kegg, reactome)
head(DE_res)
##   Protein.Name        logFC  AveExpr          t      P.Value  adj.P.Val
## 1         ABL1  0.016198841 2.148463  0.9153406 0.3607002121 0.59373515
## 2         ABL2 -0.007336859 3.245519 -0.7138443 0.4758423919 0.68920568
## 3         ACE2  0.007742940 2.755534  0.4798996 0.6316257721 0.79677100
## 4         ACTH  0.006590667 2.650429  0.3888161 0.6976700768 0.83479717
## 5       ADAM12  0.002574039 2.663372  0.1578207 0.8746973233 0.93334438
## 6  Adiponectin -0.091616177 3.555267 -3.8473203 0.0001440704 0.01216739
##           B           sig SYMBOL
## 1 -6.087681 insignificant   ABL1
## 2 -6.248323 insignificant   ABL2
## 3 -6.385116 insignificant   ACE2
## 4 -6.423896 insignificant   POMC
## 5 -6.485802 insignificant ADAM12
## 6  0.594801   significant ADIPOQ
library(dplyr)
DE_res <- DE_res %>%
  filter(!is.na(SYMBOL)) %>%
  group_by(SYMBOL) %>%
  arrange(P.Value) %>%
  filter(row_number() == 1) %>%
  data.frame()

gene_list <- DE_res$t
names(gene_list) <- DE_res$SYMBOL
gene_list <- sort(gene_list, decreasing = T)
head(gene_list, 5)
##      PRL       C3   UBE2G2      CRP      LPO 
## 4.278304 3.613029 3.579063 3.476249 3.343124
options(warn=-1)
fgseaRes <- fgsea(pathways = pathways, stats = gene_list, minSize = 15, 
                 maxSize = 500, nperm = 10000, gseaParam = 1)
fgseaRes <- fgseaRes[order(fgseaRes$pval), ]

collapsedPathways <- collapsePathways(fgseaRes = fgseaRes[padj < 0.05], 
                                      pathways = pathways, stats = gene_list)
mainPathways <- fgseaRes[pathway %in% collapsedPathways$mainPathways] # keep results of independent pathways

top_pathway <- mainPathways %>%
  filter(padj < 0.05 & abs(NES) > 1) %>%
  arrange(NES)

top_pathway$pathway <- factor(top_pathway$pathway, levels = top_pathway$pathway)
ggplot(top_pathway, aes(y = NES, x = pathway)) +
  geom_bar(width = 0.8, position = position_dodge(width = 0.8), stat = "identity", fill = "blue") + 
  coord_flip() +
  theme_bw() +
  theme(
    axis.title.y = element_blank()
  )

plotEnrichment(pathway = pathways[["KEGG_CHEMOKINE_SIGNALING_PATHWAY"]], stats = gene_list) + 
  labs(title="KEGG_CHEMOKINE_SIGNALING_PATHWAY")

top_pathway %>% 
  filter(pathway=="KEGG_CHEMOKINE_SIGNALING_PATHWAY") %>% 
  dplyr::select(leadingEdge) %>% 
  unlist() %>% 
  unname()
##  [1] "CCL22"  "CCL27"  "CCL5"   "CSK"    "RAC1"   "MAPK1"  "STAT1"  "STAT3" 
##  [9] "PRKACA" "CRK"    "LYN"    "VAV1"   "CCL18"  "CCL17"  "FGR"    "SHC1"  
## [17] "PIK3CG"

Classification Model of the Disease (PD vs Normal)

Let’s train the model using top 8 proteins + clinical variates like age + sex + disease duration

setwd('/Users/junchaoshen/UPenn BMI')
library(caret)
library(e1071)
library(precrec)
library(ROCit)

utsw = protein.log[protein.log$Site == 'UTSW',]
penn = protein.log[protein.log$Site == 'PennState',]


### Let's train the model using 4 proteins that passed the p value threshold of 0.05 on both cohorts
train_control <- trainControl(method = "repeatedcv", number = 5, repeats = 50)
model <- train(as.factor(Group) ~
                        Age+
                        ApoM+
                        BSP+
                        OMD+
                        NCAM.120+
                        IDS+
                        IGFBP.2+
                        FCN1,
                        method = "glm",
                        data = utsw,
                        na.action=na.omit,
                        family = binomial)

# Let's take a look at the final model
summary(model)
## 
## Call:
## NULL
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5302   0.2372   0.3796   0.6345   1.3347  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -24.71861   12.36014  -2.000  0.04551 * 
## Age           0.02957    0.02906   1.017  0.30894   
## ApoM         -1.80507    2.10129  -0.859  0.39032   
## BSP           1.94530    1.82972   1.063  0.28770   
## OMD          -1.10608    2.30227  -0.480  0.63092   
## NCAM.120      3.75151    3.49502   1.073  0.28310   
## IDS           1.11857    2.29752   0.487  0.62636   
## IGFBP.2       4.52205    1.63369   2.768  0.00564 **
## FCN1         -0.90673    2.16967  -0.418  0.67601   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 128.28  on 139  degrees of freedom
## Residual deviance: 107.55  on 131  degrees of freedom
## AIC: 125.55
## 
## Number of Fisher Scoring iterations: 5
# Plot ROC curve with AUC score
options(repr.plot.width=8, repr.plot.height=8)
predict_train1 <- predict(model, newdata = utsw, type= "prob")
predict_test1 <- predict(model, newdata = penn, type= "prob")
precrec_obj1 <- rocit(class = utsw$Group, score = predict_train1$PD)
precrec_obj2 <- rocit(class = penn$Group, score = predict_test1$PD)
plot(precrec_obj1,col = 'Red')

plot(precrec_obj2,col = 'Blue')

summary(precrec_obj1)
##                            
##  Method used: empirical    
##  Number of positive(s): 116
##  Number of negative(s): 24 
##  Area under curve: 0.7809
summary(precrec_obj2)
##                            
##  Method used: empirical    
##  Number of positive(s): 100
##  Number of negative(s): 79 
##  Area under curve: 0.5989

Longitudinal Analysis

MoCA ~ Top 20 proetins quartile X Days from Baseline + Age + Sex + Disease Duration

library(sjPlot)
library(sjmisc)
library(nlme)
library(lme4)

Longitudinal = read.csv('UTSW MoCA Longitudinal Final.csv')
kable(Longitudinal[1:5,], "html") %>% kable_styling("striped") %>% scroll_box(width = "100%")
GUID YearstoVisit MoCATotal MoCA_BL Group SampleDate AgeAtSample.x DiseaseDuration Sex a1.Antitrypsin a2.Antiplasmin a2.HS.Glycoprotein a2.Macroglobulin ABL1 ACE2 ACTH Activated.Protein.C Activin.A Activin.AB ADAM.9 ADAM12 ADAMTS.4 ADAMTS.5 Adiponectin Afamin Aflatoxin.B1.aldehyde.reductase Aggrecan AIF1 AIP AK1A1 Albumin ALCAM ALK.1 ALT Aminoacylase.1 AMNLS AMPK.a1b1g1 AMPK.a2b2g1 AMPM2 amyloid.precursor.protein AN32B Angiogenin Angiopoietin.1 Angiopoietin.2 Angiopoietin.4 Angiostatin Angiotensinogen ANGL3 ANGL4 annexin.I annexin.II annexin.VI Antithrombin.III Apo.A.I Apo.B Apo.D Apo.E Apo.E3 AREG ARGI1 ARI3A ARMEL ARP19 ARSB ART Artemin ARTS1 Arylsulfatase.A ASAH2 ASAHL ASGR1 ASM3A ATS1 ATS13 ATS15 AURKB Aurora.kinase.A Azurocidin b.ECGF b.Endorphin b.NGF b2.Microglobulin B7.2 B7.H1 B7.H2 BAFF BARK1 BCAM Bcl.2 BCL2.like.1.protein BCMA BDNF bFGF BFL1 BGH3 BGN BLC BMP.1 BMP.7 BMP.RII BMPER BMPR1A BMX BNP.32 BOC Bone.proteoglycan.II BPI BSP BSSP4 BST1 BTK C1.Esterase.Inhibitor C1r C2 C34.gp41.HIV.Fragment C3d C5 C5a C5b.6.Complex C7 C8 C9 Cadherin.2 Cadherin.5 Cadherin.E Calcineurin calgranulin.B Calpain.I Calpastatin CAMK2A CAMK2B CAMK2D CaMKK.alpha CAPG Carbonic.anhydrase.6 Carbonic.anhydrase.9 Carbonic.anhydrase.I carbonic.anhydrase.II Carbonic.anhydrase.III Carbonic.Anhydrase.IV Carbonic.anhydrase.VII Carbonic.Anhydrase.X Carbonic.anhydrase.XIII Caspase.10 Caspase.2 Caspase.3 Catalase CATE Cathepsin.A Cathepsin.B Cathepsin.D Cathepsin.H Cathepsin.S Cathepsin.V CATZ CBPE CCL28 CD109 CD22 CD226 CD23 CD30.Ligand CD36.ANTIGEN CD40.ligand.soluble CD48 CD5L CD70 CD97 CDC37 CDK1.cyclin.B CDK2.cyclin.A CDK5.p35 CDK8.cyclin.C CDON CFC1 Chitotriosidase.1 Chk2 CHL1 CHST6 cIAP.2 Ck.b.8.1 CK.BB CK.MB CK.MM CK2.A1.B CK2.A2.B CLC1B CLC4K CLF.1.CLC.Complex CLM6 CNDP1 CNTF CNTFR.alpha CNTN2 CO8A1 Coagulation.Factor.IX Coagulation.Factor.IXab Coagulation.Factor.VII Coagulation.Factor.X Coagulation.Factor.XI Cofilin.1 Collectin.Kidney.1 COMMD7 complement.factor.H.related.5 CONA1 contactin.1 Contactin.4 Contactin.5 COX.2 CPNE1 CRDL1 Cripto CRK CRP CRTAM CSF.1 CSK CSK21 CTACK CTAP.III CTGF CTLA.4 CXCL16.soluble Cyclin.B1 Cystatin.C Cystatin.M CYTD CYTF CYTN Cytochrome.c Cytochrome.P450.3A4 CYTT DAF DAN DAPK2 DBNL DC.SIGN DcR3 DEAD.box.protein.19B DERM DHH discoidin.domain.receptor.1 Dkk.4 DKK1 DKK3 DLC8 DLL1 DLRB1 DnaJ.homolog dopa.decarboxylase DR6 DRAK2 DRG.1 DRR1 DSC3 Dtk DUS3 Dynactin.subunit.2 ECM1 EDA EDAR EF.1.beta EG.VEGF eIF.5 eIF.5A.1 Elafin Elastase EMAP.2 EMR2 Endocan Endoglin Endostatin Endothelin.converting.enzyme.1 ENPP7 ENTP5 Eotaxin EP15R EphA1 EPHA3 EphA5 EPHAA EPHB2 EphB6 Ephrin.A4 Ephrin.A5 Ephrin.B3 EPI Epithelial.cell.kinase EPO.R ER ERBB1 ERBB2 ERBB3 ERBB4 ERK.1 ESAM Esterase.D ETHE1 FABP FABPE Factor.B Factor.I FAK1 FAM107B Fas.ligand.soluble FCAR FCG3B FCGR1 FCN1 FCN2 FCRL3 FER FETUB FGF.10 FGF.12 FGF.16 FGF.18 FGF.19 FGF.20 FGF.4 FGF.5 FGF.6 FGF.8A FGF7 FGFR.2 FGFR.3 FGFR4 FGR FLRT1 Flt.3 Flt3.ligand FN1.4 Fractalkine.CX3CL.1 FSH FST FSTL3 Fucosyltransferase.3 FUT5 FYN G.CSF.R GA733.1.protein Galectin.2 Galectin.3 Galectin.4 Galectin.8 GAPDH.liver GCKR GCP.2 GDF.11 GDF.9 GDF2 Gelsolin GFAP GFRa.1 GFRa.2 GIB GIIE GITR Glucagon Glutamate.carboxypeptidase Glutathione.S.transferase.Pi Glypican.3 GM.CSF GNS GOT1 gp130.soluble GP1BA GPC2 GPC5 gpIIbIIIa GPNMB GPVI Granulysin granzyme.A Granzyme.B Granzyme.H GRN Gro.a Gro.b.g Growth.hormone.receptor GSK.3.alpha.beta GV H6ST1 HAI.1 Haptoglobin.Mixed.Type Hat1 HCC.1 HCC.4 HCG HCK HDAC8 HDGR2 Hemopexin Heparin.cofactor.II HGF HGFA HINT1 HIPK3 Histone.H1.2 Histone.H2A.z HIV.2.Rev HMG.1 HMGR hnRNP.A.B hnRNP.A2.B1 hnRNP.K HNRPQ HO.2 HPG. HPLN1 HPV.E7.Type.16 HRG HSP.40 HSP.60 HSP.70 HSP.90a.b HTRA2 HVEM I.309 I.TAC ICOS IDE IDS IDUA IF4A3 IFN.g IFN.g.R1 IFN.lambda.1 IFN.lambda.2 IgD IgE IGF.I IGF.I.sR IGF.II.receptor IGFBP.1 IGFBP.2 IGFBP.3 IGFBP.4 IGFBP.5 IGFBP.6 IGFBP.7 IgG IgM IL.1.R.AcP IL.1.R4 IL.1.sRI IL.10 IL.11 IL.12.Rb1 IL.12.RB2 IL.13 IL.13.Ra1 IL.15.Ra IL.16 IL.17 IL.17.RC IL.17.RD IL.17.sR IL.17B IL.17B.R IL.17D IL.17E IL.17F IL.18.BPa IL.18.Ra IL.19 IL.1a IL.1F7 IL.1Rrp2 IL.2 IL.2.sRa IL.2.sRg IL.20 IL.20.Ra IL.22 IL.22BP IL.23 IL.23.R IL.27 IL.3 IL.3.Ra IL.34 IL.4 IL.4.sR IL.5.Ra IL.6 IL.6.sRa IL.7 IL.7.Ra IL.8 IL22RA1 IL24 ILT.2 ILT.4 IMB1 IMDH1 IMDH2 ING1 Insulin Integrin.a1b1 Integrin.aVb5 IP.10 IR ITI.heavy.chain.H4 JAG1 JAG2 JAK2 JAM.B JAM.C JAML1 JNK2 K.ras Kallikrein.11 kallikrein.12 kallikrein.13 Kallikrein.4 kallikrein.5 Kallikrein.6 Kallikrein.7 kallikrein.8 Kallistatin Karyopherin.a2 Keratin.18 KI2L4 KI3L2 KI3S1 KIF23 Kininogen.HMW KIRR3 KLRF1 KPCI KPCT KREM2 Ku70 KYNU Lactoferrin LAG.1 LAG.3 Lamin.B1 Laminin Layilin LBP LCK LCMT1 LD78.beta LDH.H.1 LEAP.1 Leptin LG3BP LIF.sR LIGHT LIMP.II LIN7B Lipocalin.2 LKHA4 LRIG3 LRP8 LRRT1 LRRT3 LSAMP Luteinizing.hormone LY86 LY9 Lymphotactin Lymphotoxin.a1.b2 Lymphotoxin.a2.b1 Lymphotoxin.b.R LYN LYNB Lysozyme LYVE1 M2.PK Macrophage.mannose.receptor MAPK14 MAPK2 MAPK5 MAPKAPK3 MASP3 MATK MATN2 MBD4 MBL MCP.1 MCP.3 MCP.4 MDC MDHC MDM2 MED.1 MEPE Mesothelin Met METAP1 MFGM MFRP MIA MICA MICB Midkine MIF MIP.1a MIP.3a MIP.3b MIP.5 MIS MK01 MK08 MK11 MK12 MK13 MMP.1 MMP.10 MMP.12 MMP.13 MMP.14 MMP.2 MMP.3 MMP.7 MMP.8 MMP.9 Mn.SOD MO2R1 Moesin MP2K2 MPIF.1 MRC2 MSP Myeloperoxidase Myoglobin Myokinase.human NACA NADPH.P450.Oxidoreductase NAGK NANOG NAP.2 NCAM.120 NCAM.L1 NCC27 NCK1 NDP.kinase.B Nectin.like.protein.2 NET4 Neurotrophin.3 Neurotrophin.5 NG36 NID2 Nidogen NKG2D NKp30 NKp46 NLGNX NMT1 Nogo.Receptor NOTC2 Notch.1 NovH NPS.PLA2 Nr.CAM NR1D1 NRP1 NRX1B NRX3B NSF1C NUDC3 NXPH1 OCAD1 Olfactomedin.4 OLR1 OMD ON OPG OX2G P.Selectin p27Kip1 PA2G4 PACAP.38 PAFAH PAFAH.beta.subunit PAI.1 PAK3 PAK7 PAPP.A paraoxonase.1 PARC PARK7 PBEF PCNA PCSK7 PCSK9 PD.L2 PDE11 PDE3A PDE5A PDE7A PDGF.AA PDGF.BB PDGF.CC PDGF.Rb PDK1 PDPK1 Periostin PERL Peroxiredoxin.1 Peroxiredoxin.6 Persephin PESC PF.4 PFD5 PGCB PGP9.5 PGRP.S PH PHI phosphoglycerate.kinase.1 PIGR PIK3CA.PIK3R1 PKB.a.b.g PKC.A PKC.B.II PKC.Z Plasmin Plasminogen PLCG1 PlGF PLK.1 PLPP PLXC1 PPAC PPase PPID PPIE Prekallikrein PRKACA PRL Prolactin.Receptor prostatic.binding.protein Protease.nexin.I Protein.C Protein.disulfide.isomerase Protein.disulfide.isomerase.A3 Proteinase.3 PSA PSA1 PSA2 PSA6 PSD7 PSMA PSME1 PSME3 pTEN PTH PTHrP PTK6 PTN PTP.1B PTP.1C PYY Rab.GDP.dissociation.inhibitor.beta RAC1 RAD51 RAN RANTES RAP Rb RBM39 RBP RELT Renin resistin RET RGM.C RGMA RGMB ROBO2 ROBO3 ROR1 RPS6KA3 RS3 RS7 RSK.like.protein.kinase RSPO2 RUXF SAA SAP SARP.2 SBDS sCD163 sCD4 SCF.sR SCGF.alpha SCGF.beta SDF.1 sE.Selectin SE6L2 Secretin Semaphorin.3A Semaphorin.3E Semaphorin.6A SEPR sFRP.3 SGTA SH21A SHBG SHC1 SHP.2 Sialoadhesin sICAM.1 sICAM.2 sICAM.5 SIG14 Siglec.3 Siglec.6 Siglec.7 Siglec.9 SKP1 sL.Selectin SLAF6 SLAF7 sLeptin.R SLIK5 SLPI SNAA SOD Somatostatin.28 Sonic.Hedgehog SORC2 Sorting.nexin.4 SP.D SPARCL1 Sphingosine.kinase.1 SPHK2 SPINT2 Spondin.1 SPTA2 sRAGE sRANKL SRCN1 SREC.I SREC.II SSRP1 ST4S6 Stanniocalcin.1 sTie.1 sTie.2 STRATIFIN Stress.induced.phosphoprotein.1 STX1a suPAR TAFI TAJ TAK1.TAB1 TARC tau TBK1 TBP TCCR TCPTP TCTP TEC TECK Tenascin Testican.1 Testican.2 TF TFF3 TFPI TGF.b.R.II TGF.b.R.III TGF.b1 TGF.b2 Thrombin Thrombopoietin.Receptor Thrombospondin.1 Thymidine.kinase Thyroglobulin Thyroxine.Binding.Globulin TIG2 TIMD3 TIMP.1 TIMP.2 TIMP.3 TLR4.MD.2.complex TNF.a TNF.b TNF.sR.I TNF.sR.II TNFSF15 TNFSF18 Topoisomerase.I tPA TPSB2 TPSG1 TRAIL.R4 transcription.factor.MLR1.isoform.CRA_b Transketolase TrATPase Triosephosphate.isomerase TrkA TrkB TrkC Tropomyosin.1.alpha.chain Tropomyosin.2 TRY3 Trypsin Trypsin.2 TS TSG.6 TSH TSLP TSLP.R TSP2 TSP4 TWEAK TWEAKR TXD12 UB2L3 UBC9 UBE2N Ubiquitin.1 UFC1 UFM1 ULBP.1 ULBP.2 ULBP.3 UNC5H3 UNC5H4 uPA URB Vasoactive.Intestinal.Peptide VCAM.1 VEGF VEGF.C VEGF.sR2 VEGF.sR3 VEGF121 WFKN2 WIF.1 WNT7A X14.3.3 X3HIDH X4.1BB X4.1BB.ligand X41 X4EBP2 X6.Phosphogluconate.dehydrogenase X6Ckine XEDAR XPNPEP1 XTP3A YES ZAP70
PDAG965NUZ 0 28 28 PD BL 76.3 2 Male 3.107447 3.244623 3.257871 4.028018 2.138934 2.742804 2.661245 2.950121 3.539176 2.906281 4.115384 2.646991 2.233250 2.700098 3.743322 4.421191 3.873605 2.672929 3.522679 4.153757 3.689611 4.275760 4.250271 3.128238 3.580914 3.564678 2.226342 2.632255 4.088699 4.642884 4.448134 2.253822 3.461529 2.964495 2.074085 2.870930 4.301919 3.745403 2.615108 3.627642 3.959194 3.742851 4.175460 5.124240 4.220587 4.083058 3.691285 4.619448 5.412079 3.626422 3.360139 2.933588 3.555421 3.542850 2.934195 3.206502 2.708166 4.939658 3.170643 3.484043 3.974314 3.084719 3.624138 2.60206 3.441915 2.586362 2.872448 3.057324 2.489255 2.270912 2.908163 2.754195 3.232183 3.022799 3.010427 3.269746 3.134528 4.079601 3.255851 3.198272 3.297848 3.995745 3.174060 3.195401 3.056600 4.623084 3.385124 3.361369 3.473735 2.960756 2.703635 2.709015 2.962653 2.506505 2.776483 3.097708 3.595210 2.890141 3.563113 3.281056 4.029591 4.281095 4.118003 3.558589 3.430945 2.903036 3.907589 3.915131 3.848528 2.693903 3.264676 3.514335 4.914363 3.243137 4.045745 4.823241 3.417887 3.587442 4.796785 3.634437 3.317938 3.718377 4.231419 2.724849 2.946550 3.673638 3.059488 4.121750 2.375846 3.243137 3.294643 2.503382 2.814580 3.79563 3.612487 2.570076 4.334844 4.213696 2.625827 4.065587 3.290168 3.022305 3.055531 3.029871 3.107549 3.760762 3.221284 3.736898 3.698796 2.980594 2.954532 3.275219 3.305201 3.970147 3.143702 2.848559 3.452078 2.451172 3.262024 2.817235 2.882354 3.008600 3.105783 3.326929 3.775982 3.543820 3.438874 2.583085 4.175526 2.429914 2.374748 4.729247 2.851503 2.529302 2.722058 4.154512 2.758230 4.483590 2.257439 2.678973 3.642880 3.915088 2.342225 3.919225 3.077876 2.741309 3.795943 3.643008 2.541579 3.347642 3.192874 4.099570 3.873838 3.617231 3.161518 2.849296 2.508395 3.656778 2.825880 2.736556 3.560301 3.611936 3.332560 4.030158 4.300532 3.121396 3.085825 4.212105 2.879726 3.132804 4.715550 3.304857 2.067071 4.070396 2.972203 3.388918 4.088459 3.98200 2.915347 3.118430 3.399154 4.393017 4.275675 4.205226 2.992554 3.677698 3.632822 3.391641 2.757548 3.840495 3.575257 2.906119 2.570309 3.933184 4.589664 3.890583 3.209327 3.344196 3.526494 2.878292 2.503109 3.874296 3.641920 4.794521 2.485580 2.533263 2.742175 3.591309 3.128076 4.384705 3.173536 3.297323 2.838093 2.236033 3.128722 4.67242 3.934685 2.723045 3.574610 2.824841 3.161996 3.152625 4.710611 4.287889 3.681440 3.603437 3.994344 2.960614 3.603772 2.139564 2.899054 2.590396 4.268814 3.231699 3.221649 3.472522 2.966470 2.992421 3.453853 2.582631 2.888685 4.396806 2.635484 2.474944 2.221153 4.609417 3.679210 3.583754 3.510277 4.077793 2.949000 4.397676 4.627936 2.502017 3.392873 2.689930 2.351603 3.123198 2.451326 3.361388 3.047002 2.564074 3.894720 3.662890 2.368844 2.325516 3.541217 2.431203 3.706590 2.574263 2.418135 2.668759 2.649627 3.505069 3.338934 2.386677 2.959089 3.075182 3.855876 3.056905 3.699933 2.663889 4.925982 3.453318 2.885191 2.934549 3.793595 2.772395 4.081693 4.406629 2.978409 2.675870 2.701309 4.290909 2.830653 2.718419 5.083698 2.678791 3.071514 3.814261 2.773055 3.428588 3.023746 3.545851 2.977815 3.710033 2.856910 2.491782 2.876622 3.649646 3.346196 3.773574 3.023664 2.470410 3.444154 3.477541 3.816778 3.245044 2.201397 2.867055 3.247065 3.704005 4.217894 3.143296 3.342778 2.312600 2.380392 4.374937 3.937513 3.030883 2.851992 4.375397 2.540329 3.370402 3.881738 4.565699 2.569959 3.806899 4.36128 2.773347 2.812646 3.376194 2.854670 2.997561 3.564595 3.083789 3.237066 3.211334 2.744449 2.906065 3.177623 2.233504 3.738820 2.557507 3.239049 3.529777 2.465234 3.451955 2.641871 3.167258 3.078964 2.549861 3.636829 3.118628 4.505401 3.829561 4.033649 3.882229 2.853211 3.448258 3.424081 2.839227 3.336260 3.607434 3.117636 2.732796 2.893373 2.672652 2.580012 2.170555 3.232055 3.631068 3.039850 3.896692 4.454177 2.852541 3.045010 3.228092 3.727777 3.337240 2.853881 4.711061 5.352353 3.533861 4.440429 3.965051 3.999939 2.267875 2.468347 2.704150 3.197556 2.891760 3.487379 2.903578 2.513750 2.566909 2.999696 2.731508 3.003504 2.214844 3.113709 2.331022 2.995855 2.808751 3.966381 3.979303 3.725912 2.838723 3.315508 2.820464 2.953566 3.017576 3.650366 2.605305 2.445604 2.926805 3.200905 2.896692 3.721093 2.849726 3.541417 3.164739 2.833275 2.776120 2.339253 3.116143 2.819873 4.210891 2.381657 2.462997 2.885305 2.948560 2.468495 3.612953 3.911216 4.084337 3.414372 3.528981 4.500316 2.168203 3.329967 3.025347 3.140697 3.910090 4.658601 2.781396 2.615108 5.068082 3.934655 3.799458 2.753813 3.018451 3.046807 3.029546 3.649783 3.153479 2.472171 2.259594 3.360385 3.086609 3.452752 4.629357 2.488410 3.028978 3.068557 2.477121 2.633064 3.066624 4.264485 2.494294 2.543820 3.606714 3.923441 3.595198 2.406029 2.869114 4.643145 2.630835 3.718294 2.920489 3.460372 3.194098 4.967117 2.549861 3.258374 3.179121 3.165778 4.244161 3.552729 2.874598 3.666031 2.863680 2.791059 2.645815 4.361326 3.019116 3.752148 3.345805 2.066326 2.281261 3.224533 2.928293 3.732185 3.317478 2.557146 1.995635 3.265549 3.749651 4.282157 4.935629 5.021235 2.566438 4.801784 4.146723 3.539841 4.127254 2.803457 3.309098 3.707229 2.555094 4.022989 2.687796 4.29389 2.918764 2.834166 2.620448 3.121691 4.432912 2.443420 4.851275 2.360783 2.597805 3.971749 3.863579 2.869642 3.313783 3.447778 2.437275 2.900531 4.314379 3.215558 2.870872 2.159266 2.508530 3.690223 4.030515 4.141152 3.265855 2.915189 3.145103 2.689398 2.913867 2.838786 3.217905 2.750200 3.034548 3.923487 2.656577 3.255972 3.091561 3.926224 4.732555 2.957703 3.326356 3.081347 3.364101 3.543484 3.609007 4.292916 3.090681 4.471723 3.938970 3.476846 3.040919 2.267406 4.260586 4.178102 2.692671 4.007261 3.050534 4.534979 3.371326 2.926342 2.516800 2.335859 2.854670 3.563576 3.863828 2.690196 2.702775 3.354780 2.971879 3.706163 3.096319 3.020693 4.341318 2.835183 3.490647 4.216324 2.740442 3.492956 2.930440 3.042575 2.957703 2.703205 4.006235 3.023335 2.207095 3.216562 3.847881 4.581079 4.156458 3.383690 4.486317 2.845408 3.431942 2.322219 2.954484 3.867243 3.255128 3.036868 2.896747 4.299568 2.305566 3.753721 2.915347 2.709440 2.285332 3.241073 2.800373 3.528698 3.61515 3.175367 4.428803 2.617839 4.478188 4.836065 2.609167 3.896162 3.333830 3.498352 3.829715 2.960281 3.456897 2.601625 2.455606 2.332236 4.941254 2.604442 3.291702 3.340801 2.660486 3.446413 4.434666 3.699031 3.478249 2.971832 3.939350 4.862610 4.180613 2.959375 2.893484 3.631677 2.711976 2.495128 2.275772 3.729149 2.871047 4.028592 4.547978 4.439475 3.263731 4.357849 3.446428 3.243237 3.098921 3.909283 3.313825 2.938920 2.364739 3.768771 3.672670 3.550937 2.781325 2.825361 3.077077 2.562412 2.718834 3.136847 2.444201 2.856064 3.364082 2.996293 2.406370 3.183555 3.579521 4.006907 3.479446 4.382887 4.694308 3.371234 3.966085 4.760098 2.950949 3.374455 2.572988 2.508530 3.374638 2.927114 3.177883 2.883661 3.041116 4.401597 3.341949 3.564346 2.571010 2.880356 3.327604 2.982723 3.000434 2.453624 2.583539 3.440106 2.870989 4.565019 3.61272 4.436455 3.486175 2.564429 4.221589 3.647324 3.271818 3.708191 4.132804 3.542912 2.294466 3.258182 3.567626 4.013650 2.807332 3.447173 3.755112 3.592521 3.710211 4.356315 4.08508 2.006038 3.581802 3.155761 3.114044 4.311050 3.570263 2.264582 3.052078 4.149933 3.746595 3.748328 2.649919 3.558457 3.575142 3.477367 4.451323 4.096382 3.100164 3.193903 3.141983 2.622214 4.285746 3.768231 4.096128 4.361992 2.769451 3.264534 3.359399 2.276462 2.829239 3.405039 5.008653 3.960066 2.453930 2.656481 3.689531 3.287555 3.892968 3.244005 2.867114 4.102348 2.533772 4.047041 4.155278 2.879096 3.642583 3.186391 2.259116 2.685383 2.728516 3.131234 2.546049 4.282808 3.951774 3.126586 4.229869 3.505991 2.955976 3.206313 4.026835 4.588745 2.911104 4.457743 3.289767 2.402433 3.166223 2.386142 3.814946 2.745933 2.608312 4.574163 3.319730 3.920217 1.845718 2.603469 3.134273 3.597179 2.712060 2.644340 3.058236 4.024986 2.945222 2.809492 2.706376 2.752509 3.362162 2.278982 2.984797 2.362105 4.785282 3.907320 4.085879 3.525524 3.004665 3.766903 2.988826 3.289745 2.660391 3.634518 3.290747 2.560385 3.185174 2.760272 2.626853 3.180986 4.143517 2.492481 2.872972 2.259594 3.327829 3.345942 3.499701 3.937262 3.516628 4.422197 3.906965 2.644143 2.689753 2.972249 3.574714 3.759358 3.290880 3.424784 2.592399 3.988791 4.077829 3.205204 3.746385 3.814161 2.827821 3.857405 3.438067 2.439491 3.888045 2.782974 2.494294 2.544192 3.908790 2.459242 4.356254 4.330959 2.89867 3.882308 4.046171 3.117039 4.069383
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4.406629 2.978409 2.675870 2.701309 4.290909 2.830653 2.718419 5.083698 2.678791 3.071514 3.814261 2.773055 3.428588 3.023746 3.545851 2.977815 3.710033 2.856910 2.491782 2.876622 3.649646 3.346196 3.773574 3.023664 2.470410 3.444154 3.477541 3.816778 3.245044 2.201397 2.867055 3.247065 3.704005 4.217894 3.143296 3.342778 2.312600 2.380392 4.374937 3.937513 3.030883 2.851992 4.375397 2.540329 3.370402 3.881738 4.565699 2.569959 3.806899 4.36128 2.773347 2.812646 3.376194 2.854670 2.997561 3.564595 3.083789 3.237066 3.211334 2.744449 2.906065 3.177623 2.233504 3.738820 2.557507 3.239049 3.529777 2.465234 3.451955 2.641871 3.167258 3.078964 2.549861 3.636829 3.118628 4.505401 3.829561 4.033649 3.882229 2.853211 3.448258 3.424081 2.839227 3.336260 3.607434 3.117636 2.732796 2.893373 2.672652 2.580012 2.170555 3.232055 3.631068 3.039850 3.896692 4.454177 2.852541 3.045010 3.228092 3.727777 3.337240 2.853881 4.711061 5.352353 3.533861 4.440429 3.965051 3.999939 2.267875 2.468347 2.704150 3.197556 2.891760 3.487379 2.903578 2.513750 2.566909 2.999696 2.731508 3.003504 2.214844 3.113709 2.331022 2.995855 2.808751 3.966381 3.979303 3.725912 2.838723 3.315508 2.820464 2.953566 3.017576 3.650366 2.605305 2.445604 2.926805 3.200905 2.896692 3.721093 2.849726 3.541417 3.164739 2.833275 2.776120 2.339253 3.116143 2.819873 4.210891 2.381657 2.462997 2.885305 2.948560 2.468495 3.612953 3.911216 4.084337 3.414372 3.528981 4.500316 2.168203 3.329967 3.025347 3.140697 3.910090 4.658601 2.781396 2.615108 5.068082 3.934655 3.799458 2.753813 3.018451 3.046807 3.029546 3.649783 3.153479 2.472171 2.259594 3.360385 3.086609 3.452752 4.629357 2.488410 3.028978 3.068557 2.477121 2.633064 3.066624 4.264485 2.494294 2.543820 3.606714 3.923441 3.595198 2.406029 2.869114 4.643145 2.630835 3.718294 2.920489 3.460372 3.194098 4.967117 2.549861 3.258374 3.179121 3.165778 4.244161 3.552729 2.874598 3.666031 2.863680 2.791059 2.645815 4.361326 3.019116 3.752148 3.345805 2.066326 2.281261 3.224533 2.928293 3.732185 3.317478 2.557146 1.995635 3.265549 3.749651 4.282157 4.935629 5.021235 2.566438 4.801784 4.146723 3.539841 4.127254 2.803457 3.309098 3.707229 2.555094 4.022989 2.687796 4.29389 2.918764 2.834166 2.620448 3.121691 4.432912 2.443420 4.851275 2.360783 2.597805 3.971749 3.863579 2.869642 3.313783 3.447778 2.437275 2.900531 4.314379 3.215558 2.870872 2.159266 2.508530 3.690223 4.030515 4.141152 3.265855 2.915189 3.145103 2.689398 2.913867 2.838786 3.217905 2.750200 3.034548 3.923487 2.656577 3.255972 3.091561 3.926224 4.732555 2.957703 3.326356 3.081347 3.364101 3.543484 3.609007 4.292916 3.090681 4.471723 3.938970 3.476846 3.040919 2.267406 4.260586 4.178102 2.692671 4.007261 3.050534 4.534979 3.371326 2.926342 2.516800 2.335859 2.854670 3.563576 3.863828 2.690196 2.702775 3.354780 2.971879 3.706163 3.096319 3.020693 4.341318 2.835183 3.490647 4.216324 2.740442 3.492956 2.930440 3.042575 2.957703 2.703205 4.006235 3.023335 2.207095 3.216562 3.847881 4.581079 4.156458 3.383690 4.486317 2.845408 3.431942 2.322219 2.954484 3.867243 3.255128 3.036868 2.896747 4.299568 2.305566 3.753721 2.915347 2.709440 2.285332 3.241073 2.800373 3.528698 3.61515 3.175367 4.428803 2.617839 4.478188 4.836065 2.609167 3.896162 3.333830 3.498352 3.829715 2.960281 3.456897 2.601625 2.455606 2.332236 4.941254 2.604442 3.291702 3.340801 2.660486 3.446413 4.434666 3.699031 3.478249 2.971832 3.939350 4.862610 4.180613 2.959375 2.893484 3.631677 2.711976 2.495128 2.275772 3.729149 2.871047 4.028592 4.547978 4.439475 3.263731 4.357849 3.446428 3.243237 3.098921 3.909283 3.313825 2.938920 2.364739 3.768771 3.672670 3.550937 2.781325 2.825361 3.077077 2.562412 2.718834 3.136847 2.444201 2.856064 3.364082 2.996293 2.406370 3.183555 3.579521 4.006907 3.479446 4.382887 4.694308 3.371234 3.966085 4.760098 2.950949 3.374455 2.572988 2.508530 3.374638 2.927114 3.177883 2.883661 3.041116 4.401597 3.341949 3.564346 2.571010 2.880356 3.327604 2.982723 3.000434 2.453624 2.583539 3.440106 2.870989 4.565019 3.61272 4.436455 3.486175 2.564429 4.221589 3.647324 3.271818 3.708191 4.132804 3.542912 2.294466 3.258182 3.567626 4.013650 2.807332 3.447173 3.755112 3.592521 3.710211 4.356315 4.08508 2.006038 3.581802 3.155761 3.114044 4.311050 3.570263 2.264582 3.052078 4.149933 3.746595 3.748328 2.649919 3.558457 3.575142 3.477367 4.451323 4.096382 3.100164 3.193903 3.141983 2.622214 4.285746 3.768231 4.096128 4.361992 2.769451 3.264534 3.359399 2.276462 2.829239 3.405039 5.008653 3.960066 2.453930 2.656481 3.689531 3.287555 3.892968 3.244005 2.867114 4.102348 2.533772 4.047041 4.155278 2.879096 3.642583 3.186391 2.259116 2.685383 2.728516 3.131234 2.546049 4.282808 3.951774 3.126586 4.229869 3.505991 2.955976 3.206313 4.026835 4.588745 2.911104 4.457743 3.289767 2.402433 3.166223 2.386142 3.814946 2.745933 2.608312 4.574163 3.319730 3.920217 1.845718 2.603469 3.134273 3.597179 2.712060 2.644340 3.058236 4.024986 2.945222 2.809492 2.706376 2.752509 3.362162 2.278982 2.984797 2.362105 4.785282 3.907320 4.085879 3.525524 3.004665 3.766903 2.988826 3.289745 2.660391 3.634518 3.290747 2.560385 3.185174 2.760272 2.626853 3.180986 4.143517 2.492481 2.872972 2.259594 3.327829 3.345942 3.499701 3.937262 3.516628 4.422197 3.906965 2.644143 2.689753 2.972249 3.574714 3.759358 3.290880 3.424784 2.592399 3.988791 4.077829 3.205204 3.746385 3.814161 2.827821 3.857405 3.438067 2.439491 3.888045 2.782974 2.494294 2.544192 3.908790 2.459242 4.356254 4.330959 2.89867 3.882308 4.046171 3.117039 4.069383
PDAL120HLU 0 29 29 PD BL 64.5 13 Female 2.949926 3.300943 3.388492 4.062589 2.023252 2.641672 2.552790 2.776919 3.782637 2.903416 3.165066 2.533263 2.135769 2.585686 3.839195 4.611272 3.709041 2.710710 3.383995 3.902596 3.680979 3.942276 4.224657 3.029018 3.508637 3.515582 2.344196 2.415474 4.011211 4.588042 4.377071 2.228400 3.627233 2.864630 2.275542 2.631748 4.212643 3.655110 2.480151 3.542240 3.845514 4.130459 4.008042 5.116426 4.192827 3.804085 3.554089 4.442785 5.316172 3.540455 2.971183 2.794488 3.534102 3.389503 4.422069 3.340385 2.638090 5.026072 2.983807 3.784967 3.764154 3.109848 3.351352 2.47261 3.485764 2.487280 2.762529 2.838912 2.342620 2.188366 2.859138 2.632052 3.328094 2.998346 2.955784 3.200467 3.546777 4.112256 3.268461 3.072103 3.158061 3.839685 2.965578 3.326561 2.903633 4.464738 3.558864 3.293164 3.318126 2.692759 2.586587 2.517064 2.876276 2.413300 2.643847 3.271795 3.630845 2.473633 3.802240 3.233047 4.180241 4.204362 3.863977 3.546814 3.480237 2.812713 3.785501 3.940696 3.816056 2.764624 3.233453 3.409493 4.896532 3.095100 4.183384 4.733404 3.270213 3.520248 4.704499 3.702715 3.258637 3.632397 4.190304 2.522183 3.148232 3.854695 3.661178 3.804671 2.206826 3.283799 3.306425 2.455910 2.756712 3.80388 3.541991 2.487421 4.473008 3.776774 2.537063 3.803887 3.246548 2.785757 3.032377 2.820530 3.054077 3.632548 3.261382 3.855295 3.796429 2.829239 2.871047 3.405893 3.389326 3.983261 2.498862 2.712986 3.166045 2.265054 3.185316 2.629410 2.761251 2.442793 3.013890 3.196342 3.858910 3.443091 3.216271 2.121888 4.146103 2.385785 2.209247 4.798121 2.712397 2.507586 2.595717 3.996117 2.758533 4.238914 2.175512 2.546543 3.706812 3.764393 2.213252 3.950866 3.082283 2.894372 4.010758 3.850168 2.712650 3.716846 3.106055 4.061833 3.672467 3.580012 2.970114 2.589167 2.483730 3.589078 2.693551 2.579326 3.682461 3.463744 2.614581 4.162549 4.094174 2.766190 3.058350 4.118784 3.303390 3.028449 4.433631 3.220291 2.053846 4.188172 2.490801 3.468362 3.793776 3.68946 2.762604 3.127558 3.443185 4.249184 4.433808 4.244455 3.127267 3.506857 3.516958 3.288227 2.744606 3.760882 3.728768 2.766413 2.520090 3.820044 4.505976 3.726099 2.462548 3.268438 3.643275 2.698622 2.373280 3.942087 3.572325 4.690978 2.373464 2.397766 2.868938 3.595584 3.064982 4.314621 3.265761 2.997343 2.790426 2.225309 3.145538 4.66639 3.773465 2.430720 3.349239 2.879612 3.147120 3.269910 4.788306 4.184796 3.789856 3.558493 4.147358 2.935558 4.043716 2.064083 2.799065 2.405688 4.339938 3.203604 3.285850 3.621944 2.853272 2.916822 3.602114 2.485438 2.719165 4.363294 2.513617 2.493737 2.121888 4.652776 3.613408 3.435669 3.489241 4.552880 2.796088 4.369268 4.584553 2.429591 3.467312 2.662002 2.476107 3.360726 2.356026 3.516284 3.076822 2.522314 3.748025 3.818219 2.248709 2.310906 3.422754 2.378034 4.287385 2.467904 2.266702 2.517592 2.539452 2.604010 3.315088 2.655235 2.830717 2.784546 2.938970 2.948560 3.524734 2.484442 4.689599 3.485395 3.948066 2.825556 3.920645 2.803662 3.696339 4.283369 2.804548 2.478711 2.561698 4.347240 2.723948 2.642761 5.003971 2.593729 3.133986 3.033906 2.480294 3.519776 3.008643 2.611405 3.108159 3.765549 2.999522 2.393400 2.810300 3.816997 3.045049 3.789341 2.860038 2.482302 3.376796 3.447034 3.885638 3.092896 2.046495 2.792882 2.993392 3.784781 4.054762 3.323108 3.393926 2.210853 2.260787 4.337014 3.346568 2.904012 3.076131 4.306093 2.422097 3.313909 3.909058 4.472597 2.341039 3.765549 4.22366 3.462143 2.332438 3.339869 2.762679 2.933538 3.418566 4.274850 3.553859 3.181472 2.554610 3.018284 3.002900 2.072617 3.751364 2.423246 3.150542 3.491376 2.260071 3.474100 2.555578 3.109106 3.202761 2.558108 3.747241 2.974189 4.454973 3.910758 3.990068 3.749365 2.662380 3.346431 3.404919 2.745777 3.279233 3.664068 3.064233 2.618885 2.928805 2.717671 2.450095 2.145818 3.587172 3.975216 3.130076 3.747326 4.473580 2.678700 3.005438 3.442056 3.727208 3.347701 2.897022 4.725398 5.211216 3.260167 4.490393 3.670496 4.083273 2.179552 2.395326 2.605197 2.772468 2.680879 3.394539 2.875351 2.422426 2.433770 2.909181 2.603794 3.033986 2.087426 2.948413 2.323046 2.405175 2.772468 4.007244 4.045847 3.631373 2.726157 3.265384 2.918607 3.304383 2.903524 3.550130 2.496237 2.382377 2.900367 3.425942 3.218929 3.400158 2.719331 3.064757 3.076058 2.776265 2.604658 2.265054 3.158543 2.572755 4.213125 2.053846 2.477411 2.839918 2.754042 2.296884 3.628348 4.049299 4.224030 3.360915 3.353801 4.370643 2.149527 3.279963 2.982452 3.172194 3.902949 4.526153 2.798029 2.329601 5.058872 4.018163 3.765065 2.573800 3.078783 2.960281 3.173827 3.733823 2.974558 2.357935 2.106191 3.369846 3.006937 3.557339 4.551267 2.377124 2.869290 3.050534 2.361161 2.475671 2.963882 4.329983 2.513617 2.458487 3.512164 3.637300 3.499233 2.552547 2.810165 4.516858 3.081239 3.803989 2.813914 3.436799 3.251784 4.906974 2.444357 3.204011 3.164918 3.110388 4.419653 4.045952 2.982588 3.591610 2.601843 2.483587 2.577951 4.181403 2.868527 3.853047 3.312050 1.980912 2.182700 3.187323 3.486898 3.687288 3.482845 2.447933 2.127105 3.310693 3.727468 4.246811 4.938724 5.000522 2.729651 4.719321 4.028409 3.467386 3.976895 2.617210 3.077513 3.670645 2.452400 4.043504 2.497621 4.50901 2.828596 2.977952 2.499687 2.959041 4.270294 2.294907 4.788094 2.295567 2.440279 3.963826 3.490983 3.024157 3.232310 3.451971 2.583426 2.733358 4.361256 3.070333 2.817896 2.010300 2.471438 3.727915 3.975308 4.030903 3.229835 2.830396 3.003504 2.662380 2.767972 2.739493 3.190892 2.764923 2.866524 3.968422 2.624076 3.128819 3.123067 3.865927 4.574702 2.957272 3.358506 3.389910 3.392063 3.666668 3.432633 4.187377 3.155063 4.242273 3.836033 3.678063 2.963646 2.086004 3.917505 4.143184 2.701136 3.858278 3.084719 4.524066 3.519027 2.844974 2.313445 2.191171 2.788098 3.359437 3.627847 2.551572 2.683497 3.538197 2.824841 3.596256 3.120969 2.827369 4.355664 2.716170 3.321619 4.194495 2.598900 3.498710 2.833019 3.012795 3.037466 2.776992 3.759894 2.911264 2.173186 3.110084 4.187312 4.262563 4.097760 3.388314 4.271904 2.898012 3.312495 2.311330 2.830973 3.764624 2.899875 2.930287 2.708931 4.193219 2.183270 3.876928 2.728273 2.672836 2.139249 3.412931 2.783403 3.555493 3.43870 3.142421 4.453748 2.518251 4.186976 4.623994 2.439648 4.116973 3.197501 3.379759 3.790144 3.060924 3.309524 2.548389 2.181272 2.245266 4.583814 2.580012 3.214500 3.180814 2.593729 4.070987 4.404190 3.647627 3.374602 2.939170 3.873739 4.941339 4.031429 2.938820 2.723948 3.548291 2.676053 2.392345 2.203033 3.619886 2.884739 4.215907 4.556774 4.519747 3.228836 4.299337 3.590452 2.712650 2.958851 3.804930 3.029992 3.038382 2.270912 3.615424 3.635986 2.608526 2.678700 2.751741 2.940417 2.607348 2.642563 3.095588 2.263636 2.784475 3.403258 2.904716 2.210586 3.098609 3.515476 3.811508 3.544998 4.259188 4.558158 3.327686 3.781612 4.628352 2.825751 3.042418 2.483016 2.346939 3.424195 2.968062 2.961136 3.065953 2.802910 4.177069 3.315088 3.491628 2.480007 2.835120 3.195678 2.736715 2.978819 2.286232 2.500374 3.431348 3.426723 4.365286 3.69668 4.419301 3.645717 2.480869 4.266820 3.407238 3.027023 3.727810 4.188740 3.085183 2.238799 3.115677 3.464057 4.108389 2.979321 3.397801 3.759683 3.610245 3.785259 4.368242 3.98772 2.040602 3.517724 3.066289 3.065878 4.373184 3.631525 2.321598 3.020900 2.376212 3.639606 3.720176 2.549003 4.578858 3.312241 3.387675 4.485271 3.916917 2.996293 3.090505 3.148880 2.438542 4.259637 3.673565 4.182763 4.234960 2.668386 3.203848 3.550827 2.087426 2.716003 2.610554 4.970531 3.975446 2.387034 2.607348 3.656424 3.372857 3.862287 3.265808 2.815179 3.920076 2.356599 4.091192 4.052659 2.943643 3.538373 3.410305 2.112270 2.496930 2.621176 3.038660 2.413467 4.212739 3.907728 3.240549 4.260434 3.521805 2.750586 3.238121 4.128984 4.682631 2.833466 4.550948 3.087107 2.249687 2.868292 2.229170 3.588854 2.595165 2.457730 4.721574 3.396461 4.031384 1.918030 2.686100 3.181929 3.681214 2.586362 2.562293 3.127914 4.075134 3.072434 2.851870 2.566673 2.418467 3.633509 2.214579 2.900804 2.275311 4.642529 3.828118 3.941074 3.346392 3.072911 3.894327 2.891314 3.258996 2.600319 3.891710 3.370717 2.482731 3.170878 2.882752 2.753200 3.113609 4.265370 2.830653 2.897242 2.135133 3.351352 3.224973 3.363029 3.775166 3.372286 4.468290 4.010292 2.521138 2.370698 2.899492 3.585822 3.760671 3.249125 3.509323 2.470704 4.066061 4.080648 3.133411 3.676602 3.695989 2.674494 3.862251 3.645697 2.239049 3.790981 2.627161 2.543944 2.455758 3.720159 2.278067 4.144213 4.368023 2.84398 4.048714 4.193417 3.074451 4.043311
PDAL120HLU 1 26 29 PD BL 64.5 13 Female 2.949926 3.300943 3.388492 4.062589 2.023252 2.641672 2.552790 2.776919 3.782637 2.903416 3.165066 2.533263 2.135769 2.585686 3.839195 4.611272 3.709041 2.710710 3.383995 3.902596 3.680979 3.942276 4.224657 3.029018 3.508637 3.515582 2.344196 2.415474 4.011211 4.588042 4.377071 2.228400 3.627233 2.864630 2.275542 2.631748 4.212643 3.655110 2.480151 3.542240 3.845514 4.130459 4.008042 5.116426 4.192827 3.804085 3.554089 4.442785 5.316172 3.540455 2.971183 2.794488 3.534102 3.389503 4.422069 3.340385 2.638090 5.026072 2.983807 3.784967 3.764154 3.109848 3.351352 2.47261 3.485764 2.487280 2.762529 2.838912 2.342620 2.188366 2.859138 2.632052 3.328094 2.998346 2.955784 3.200467 3.546777 4.112256 3.268461 3.072103 3.158061 3.839685 2.965578 3.326561 2.903633 4.464738 3.558864 3.293164 3.318126 2.692759 2.586587 2.517064 2.876276 2.413300 2.643847 3.271795 3.630845 2.473633 3.802240 3.233047 4.180241 4.204362 3.863977 3.546814 3.480237 2.812713 3.785501 3.940696 3.816056 2.764624 3.233453 3.409493 4.896532 3.095100 4.183384 4.733404 3.270213 3.520248 4.704499 3.702715 3.258637 3.632397 4.190304 2.522183 3.148232 3.854695 3.661178 3.804671 2.206826 3.283799 3.306425 2.455910 2.756712 3.80388 3.541991 2.487421 4.473008 3.776774 2.537063 3.803887 3.246548 2.785757 3.032377 2.820530 3.054077 3.632548 3.261382 3.855295 3.796429 2.829239 2.871047 3.405893 3.389326 3.983261 2.498862 2.712986 3.166045 2.265054 3.185316 2.629410 2.761251 2.442793 3.013890 3.196342 3.858910 3.443091 3.216271 2.121888 4.146103 2.385785 2.209247 4.798121 2.712397 2.507586 2.595717 3.996117 2.758533 4.238914 2.175512 2.546543 3.706812 3.764393 2.213252 3.950866 3.082283 2.894372 4.010758 3.850168 2.712650 3.716846 3.106055 4.061833 3.672467 3.580012 2.970114 2.589167 2.483730 3.589078 2.693551 2.579326 3.682461 3.463744 2.614581 4.162549 4.094174 2.766190 3.058350 4.118784 3.303390 3.028449 4.433631 3.220291 2.053846 4.188172 2.490801 3.468362 3.793776 3.68946 2.762604 3.127558 3.443185 4.249184 4.433808 4.244455 3.127267 3.506857 3.516958 3.288227 2.744606 3.760882 3.728768 2.766413 2.520090 3.820044 4.505976 3.726099 2.462548 3.268438 3.643275 2.698622 2.373280 3.942087 3.572325 4.690978 2.373464 2.397766 2.868938 3.595584 3.064982 4.314621 3.265761 2.997343 2.790426 2.225309 3.145538 4.66639 3.773465 2.430720 3.349239 2.879612 3.147120 3.269910 4.788306 4.184796 3.789856 3.558493 4.147358 2.935558 4.043716 2.064083 2.799065 2.405688 4.339938 3.203604 3.285850 3.621944 2.853272 2.916822 3.602114 2.485438 2.719165 4.363294 2.513617 2.493737 2.121888 4.652776 3.613408 3.435669 3.489241 4.552880 2.796088 4.369268 4.584553 2.429591 3.467312 2.662002 2.476107 3.360726 2.356026 3.516284 3.076822 2.522314 3.748025 3.818219 2.248709 2.310906 3.422754 2.378034 4.287385 2.467904 2.266702 2.517592 2.539452 2.604010 3.315088 2.655235 2.830717 2.784546 2.938970 2.948560 3.524734 2.484442 4.689599 3.485395 3.948066 2.825556 3.920645 2.803662 3.696339 4.283369 2.804548 2.478711 2.561698 4.347240 2.723948 2.642761 5.003971 2.593729 3.133986 3.033906 2.480294 3.519776 3.008643 2.611405 3.108159 3.765549 2.999522 2.393400 2.810300 3.816997 3.045049 3.789341 2.860038 2.482302 3.376796 3.447034 3.885638 3.092896 2.046495 2.792882 2.993392 3.784781 4.054762 3.323108 3.393926 2.210853 2.260787 4.337014 3.346568 2.904012 3.076131 4.306093 2.422097 3.313909 3.909058 4.472597 2.341039 3.765549 4.22366 3.462143 2.332438 3.339869 2.762679 2.933538 3.418566 4.274850 3.553859 3.181472 2.554610 3.018284 3.002900 2.072617 3.751364 2.423246 3.150542 3.491376 2.260071 3.474100 2.555578 3.109106 3.202761 2.558108 3.747241 2.974189 4.454973 3.910758 3.990068 3.749365 2.662380 3.346431 3.404919 2.745777 3.279233 3.664068 3.064233 2.618885 2.928805 2.717671 2.450095 2.145818 3.587172 3.975216 3.130076 3.747326 4.473580 2.678700 3.005438 3.442056 3.727208 3.347701 2.897022 4.725398 5.211216 3.260167 4.490393 3.670496 4.083273 2.179552 2.395326 2.605197 2.772468 2.680879 3.394539 2.875351 2.422426 2.433770 2.909181 2.603794 3.033986 2.087426 2.948413 2.323046 2.405175 2.772468 4.007244 4.045847 3.631373 2.726157 3.265384 2.918607 3.304383 2.903524 3.550130 2.496237 2.382377 2.900367 3.425942 3.218929 3.400158 2.719331 3.064757 3.076058 2.776265 2.604658 2.265054 3.158543 2.572755 4.213125 2.053846 2.477411 2.839918 2.754042 2.296884 3.628348 4.049299 4.224030 3.360915 3.353801 4.370643 2.149527 3.279963 2.982452 3.172194 3.902949 4.526153 2.798029 2.329601 5.058872 4.018163 3.765065 2.573800 3.078783 2.960281 3.173827 3.733823 2.974558 2.357935 2.106191 3.369846 3.006937 3.557339 4.551267 2.377124 2.869290 3.050534 2.361161 2.475671 2.963882 4.329983 2.513617 2.458487 3.512164 3.637300 3.499233 2.552547 2.810165 4.516858 3.081239 3.803989 2.813914 3.436799 3.251784 4.906974 2.444357 3.204011 3.164918 3.110388 4.419653 4.045952 2.982588 3.591610 2.601843 2.483587 2.577951 4.181403 2.868527 3.853047 3.312050 1.980912 2.182700 3.187323 3.486898 3.687288 3.482845 2.447933 2.127105 3.310693 3.727468 4.246811 4.938724 5.000522 2.729651 4.719321 4.028409 3.467386 3.976895 2.617210 3.077513 3.670645 2.452400 4.043504 2.497621 4.50901 2.828596 2.977952 2.499687 2.959041 4.270294 2.294907 4.788094 2.295567 2.440279 3.963826 3.490983 3.024157 3.232310 3.451971 2.583426 2.733358 4.361256 3.070333 2.817896 2.010300 2.471438 3.727915 3.975308 4.030903 3.229835 2.830396 3.003504 2.662380 2.767972 2.739493 3.190892 2.764923 2.866524 3.968422 2.624076 3.128819 3.123067 3.865927 4.574702 2.957272 3.358506 3.389910 3.392063 3.666668 3.432633 4.187377 3.155063 4.242273 3.836033 3.678063 2.963646 2.086004 3.917505 4.143184 2.701136 3.858278 3.084719 4.524066 3.519027 2.844974 2.313445 2.191171 2.788098 3.359437 3.627847 2.551572 2.683497 3.538197 2.824841 3.596256 3.120969 2.827369 4.355664 2.716170 3.321619 4.194495 2.598900 3.498710 2.833019 3.012795 3.037466 2.776992 3.759894 2.911264 2.173186 3.110084 4.187312 4.262563 4.097760 3.388314 4.271904 2.898012 3.312495 2.311330 2.830973 3.764624 2.899875 2.930287 2.708931 4.193219 2.183270 3.876928 2.728273 2.672836 2.139249 3.412931 2.783403 3.555493 3.43870 3.142421 4.453748 2.518251 4.186976 4.623994 2.439648 4.116973 3.197501 3.379759 3.790144 3.060924 3.309524 2.548389 2.181272 2.245266 4.583814 2.580012 3.214500 3.180814 2.593729 4.070987 4.404190 3.647627 3.374602 2.939170 3.873739 4.941339 4.031429 2.938820 2.723948 3.548291 2.676053 2.392345 2.203033 3.619886 2.884739 4.215907 4.556774 4.519747 3.228836 4.299337 3.590452 2.712650 2.958851 3.804930 3.029992 3.038382 2.270912 3.615424 3.635986 2.608526 2.678700 2.751741 2.940417 2.607348 2.642563 3.095588 2.263636 2.784475 3.403258 2.904716 2.210586 3.098609 3.515476 3.811508 3.544998 4.259188 4.558158 3.327686 3.781612 4.628352 2.825751 3.042418 2.483016 2.346939 3.424195 2.968062 2.961136 3.065953 2.802910 4.177069 3.315088 3.491628 2.480007 2.835120 3.195678 2.736715 2.978819 2.286232 2.500374 3.431348 3.426723 4.365286 3.69668 4.419301 3.645717 2.480869 4.266820 3.407238 3.027023 3.727810 4.188740 3.085183 2.238799 3.115677 3.464057 4.108389 2.979321 3.397801 3.759683 3.610245 3.785259 4.368242 3.98772 2.040602 3.517724 3.066289 3.065878 4.373184 3.631525 2.321598 3.020900 2.376212 3.639606 3.720176 2.549003 4.578858 3.312241 3.387675 4.485271 3.916917 2.996293 3.090505 3.148880 2.438542 4.259637 3.673565 4.182763 4.234960 2.668386 3.203848 3.550827 2.087426 2.716003 2.610554 4.970531 3.975446 2.387034 2.607348 3.656424 3.372857 3.862287 3.265808 2.815179 3.920076 2.356599 4.091192 4.052659 2.943643 3.538373 3.410305 2.112270 2.496930 2.621176 3.038660 2.413467 4.212739 3.907728 3.240549 4.260434 3.521805 2.750586 3.238121 4.128984 4.682631 2.833466 4.550948 3.087107 2.249687 2.868292 2.229170 3.588854 2.595165 2.457730 4.721574 3.396461 4.031384 1.918030 2.686100 3.181929 3.681214 2.586362 2.562293 3.127914 4.075134 3.072434 2.851870 2.566673 2.418467 3.633509 2.214579 2.900804 2.275311 4.642529 3.828118 3.941074 3.346392 3.072911 3.894327 2.891314 3.258996 2.600319 3.891710 3.370717 2.482731 3.170878 2.882752 2.753200 3.113609 4.265370 2.830653 2.897242 2.135133 3.351352 3.224973 3.363029 3.775166 3.372286 4.468290 4.010292 2.521138 2.370698 2.899492 3.585822 3.760671 3.249125 3.509323 2.470704 4.066061 4.080648 3.133411 3.676602 3.695989 2.674494 3.862251 3.645697 2.239049 3.790981 2.627161 2.543944 2.455758 3.720159 2.278067 4.144213 4.368023 2.84398 4.048714 4.193417 3.074451 4.043311
Longitudinal$BSP.Tertile <- with(Longitudinal, factor(
                            findInterval( BSP, c(-Inf,
                               quantile(BSP, probs=c(0.33,0.66)), Inf)), 
                            labels=c("First Tertile","Second Tertile","Third Tertile")))

# Train LME model to see how fasat and slow progressors differ
MoCA1 = lmer(MoCATotal ~ BSP.Tertile*YearstoVisit + AgeAtSample.x + Sex + as.numeric(DiseaseDuration)+ (1 | GUID), data=Longitudinal, REML=F)

data(efc)
theme_set(theme_sjplot())

p5 = plot_model(MoCA1, type = "pred", terms = c("YearstoVisit", "BSP.Tertile"),
           title = " Cognitive Change \n (UTSW + Penn State Cohort)",
           axis.title = c("YearstoVisit","Cognition(MoCA)"),
           axis.labels = NULL, legend.title = "BSP Tertile",
           wrap.title = 50, wrap.labels = 25, axis.lim = NULL, grid.breaks = NULL,
           ci.lvl = 0.9, se = NULL, colors = "Set2", order.terms = c(3,1,2)) + scale_y_continuous(limits = c(20, 30), breaks = c(20,22,24,26,28,30)) + theme_classic() +
  theme(legend.position = c(0.25, 0.2), legend.title = element_text(face = "bold", size = 16), legend.text = element_text(size = 20),
        title = element_text(size = 23),
        axis.title.x = element_text(size = 25, margin = margin(t = 10, r = 0, b = 0, l = 0)),
        axis.title.y = element_text(size = 25, margin = margin(t = 0, r = 12, b = 0, l = 0)),
        axis.text = element_text(size = 25, color = "black"), axis.line = element_line(size = 1), axis.ticks = element_line(size = 1))

p5

Longitudinal$OMD.Tertile <- with(Longitudinal, factor(
                            findInterval( OMD, c(-Inf,
                               quantile(OMD, probs=c(0.33,0.66)), Inf)), 
                            labels=c("First Tertile","Second Tertile","Third Tertile")))

# Train LME model to see how fasat and slow progressors differ
MoCA = lmer(MoCATotal ~ OMD.Tertile*YearstoVisit + AgeAtSample.x + Sex + as.numeric(DiseaseDuration)+ (1 | GUID), data=Longitudinal, REML=F)

data(efc)
theme_set(theme_sjplot())

Longitudinal$FCN1.Tertile <- with(Longitudinal, factor(
                            findInterval( FCN1, c(-Inf,
                               quantile(FCN1, probs=c(0.33,0.66)), Inf)), 
                              labels=c("First Tertile","Second Tertile","Third Tertile")))

# Train LME model to see how fasat and slow progressors differ
MoCA = lmer(MoCATotal ~ FCN1.Tertile*YearstoVisit + AgeAtSample.x + Sex + as.numeric(DiseaseDuration)+ (1 | GUID), data=Longitudinal, REML=F)

data(efc)
theme_set(theme_sjplot())

p5 = plot_model(MoCA, type = "pred", terms = c("YearstoVisit", "FCN1.Tertile"),
           title = " Cognitive Change \n (UTSW + Penn State Cohort)",
           axis.title = c("YearstoVisit","Cognition(MoCA)"),
           axis.labels = NULL, legend.title = "FCN1 Tertile",
           wrap.title = 50, wrap.labels = 25, axis.lim = NULL, grid.breaks = NULL,
           ci.lvl = 0.9, se = NULL, colors = "Set2", order.terms = c(3,1,2)) + scale_y_continuous(limits = c(20, 30), breaks = c(20,22,24,26,28,30)) + theme_classic() +
  theme(legend.position = c(0.25, 0.2), legend.title = element_text(face = "bold", size = 16), legend.text = element_text(size = 20),
        title = element_text(size = 23),
        axis.title.x = element_text(size = 25, margin = margin(t = 10, r = 0, b = 0, l = 0)),
        axis.title.y = element_text(size = 25, margin = margin(t = 0, r = 12, b = 0, l = 0)),
        axis.text = element_text(size = 25, color = "black"), axis.line = element_line(size = 1), axis.ticks = element_line(size = 1))

p5

Longitudinal$CRP.Tertile <- with(Longitudinal, factor(
                            findInterval( CRP, c(-Inf,
                               quantile(CRP, probs=c(0.33,0.66)), Inf)), 
                            labels=c("First Quartile","Second Quartile","Third Quartile")))


# Train LME model to see how fasat and slow progressors differ
MoCA = lmer(MoCATotal ~ CRP.Tertile*YearstoVisit + AgeAtSample.x + Sex + as.numeric(DiseaseDuration)+ (1 | GUID), data=Longitudinal, REML=F)

data(efc)
theme_set(theme_sjplot())

p5 = plot_model(MoCA, type = "pred", terms = c("YearstoVisit", "CRP.Tertile"),
           title = " Cognitive Change \n (UTSW + Penn State Cohort)",
           axis.title = c("YearstoVisit","Cognition(MoCA)"),
           axis.labels = NULL, legend.title = "CRP Tertile",
           wrap.title = 50, wrap.labels = 25, axis.lim = NULL, grid.breaks = NULL,
           ci.lvl = 0.9, se = NULL, colors = "Set2", order.terms = c(3,1,2)) + scale_y_continuous(limits = c(20, 30), breaks = c(20,22,24,26,28,30)) + theme_classic() +
  theme(legend.position = c(0.25, 0.17), legend.title = element_text(face = "bold", size = 16), legend.text = element_text(size = 20),
        title = element_text(size = 23),
        axis.title.x = element_text(size = 25, margin = margin(t = 10, r = 0, b = 0, l = 0)),
        axis.title.y = element_text(size = 25, margin = margin(t = 0, r = 12, b = 0, l = 0)),
        axis.text = element_text(size = 25, color = "black"), axis.line = element_line(size = 1), axis.ticks = element_line(size = 1))

Survival Analysis

Clinical Conversion into Dementia ~ Top 20 protein teritile + Age + Sex + Disease Duration

setwd('/Users/junchaoshen/UPenn BMI')
survival.upenn = read.csv('Survival Analysis Replication.csv')
kable(survival.upenn[1:5,], "html") %>% kable_styling("striped") %>% scroll_box(width = "100%")
INDDID DRSTotal BL.DRS TestDate SampleDate Event DaysVisitToSample TimeMonth AgeAtTest AgeAtSample DieseaseDuration YOB Sex LEDD_x Group a1.Antitrypsin a2.Antiplasmin a2.HS.Glycoprotein a2.Macroglobulin ABL1 ACE2 ACTH Activated.Protein.C Activin.A Activin.AB ADAM.9 ADAM12 ADAMTS.4 ADAMTS.5 Adiponectin Afamin Aflatoxin.B1.aldehyde.reductase Aggrecan AIF1 AIP AK1A1 Albumin ALCAM ALK.1 Alkaline.phosphatase.bone ALT AMHR2 Aminoacylase.1 AMNLS AMPK.a1b1g1 AMPK.a2b2g1 AMPM2 amyloid.precursor.protein AN32B Angiogenin Angiopoietin.1 Angiopoietin.2 Angiopoietin.4 Angiostatin Angiotensinogen ANGL3 ANGL4 annexin.I annexin.II annexin.VI Antithrombin.III Apo.A.I Apo.B Apo.D Apo.E Apo.E3 Apo.E4 AREG ARGI1 ARI3A ARMEL ARP19 ARSB ART Artemin ARTS1 Arylsulfatase.A ASAH2 ASAHL ASGR1 ASM3A ATS1 ATS13 ATS15 AURKB Aurora.kinase.A Azurocidin b.ECGF b.Endorphin b.NGF b2.Microglobulin B7.2 B7.H1 B7.H2 BAFF BARK1 BCAM Bcl.2 BCL2.like.1.protein BCMA BDNF bFGF BFL1 BGH3 BGN BLC BMP.1 BMP.7 BMP.RII BMPER BMPR1A BMX BNP.32 BOC Bone.proteoglycan.II BPI BSP BSSP4 BST1 BTK C1.Esterase.Inhibitor C1r C1s C2 C34.gp41.HIV.Fragment C3d C5 C5a C5b.6.Complex C7 C8 C9 Cadherin.2 Cadherin.5 Cadherin.E Calcineurin calgranulin.B Calpain.I Calpastatin CAMK2A CAMK2B CAMK2D CaMKK.alpha CAPG Carbonic.anhydrase.6 Carbonic.anhydrase.9 Carbonic.anhydrase.I carbonic.anhydrase.II Carbonic.anhydrase.III Carbonic.Anhydrase.IV Carbonic.anhydrase.VII Carbonic.Anhydrase.X Carbonic.anhydrase.XIII Caspase.10 Caspase.2 Caspase.3 Catalase CATE Cathepsin.A Cathepsin.B Cathepsin.D Cathepsin.H Cathepsin.S Cathepsin.V CATZ CBPE CCL28 CD109 CD22 CD226 CD23 CD30.Ligand CD36.ANTIGEN CD40.ligand.soluble CD48 CD5L CD70 CD97 CDC37 CDK1.cyclin.B CDK2.cyclin.A CDK5.p35 CDK8.cyclin.C CDON CFC1 Chitotriosidase.1 Chk2 CHL1 CHST6 cIAP.2 Ck.b.8.1 CK.BB CK.MB CK.MM CK2.A1.B CK2.A2.B CLC1B CLC4K CLF.1.CLC.Complex CLM6 CNDP1 CNTF CNTFR.alpha CNTN2 CO8A1 Coagulation.Factor.IX Coagulation.Factor.IXab Coagulation.Factor.VII Coagulation.Factor.X Coagulation.Factor.XI Cofilin.1 Collectin.Kidney.1 COMMD7 complement.factor.H.related.5 CONA1 contactin.1 Contactin.4 Contactin.5 COX.2 CPNE1 CRDL1 Cripto CRK CRP CRTAM CSF.1 CSK CSK21 CTACK CTAP.III CTGF CTLA.4 CXCL16.soluble Cyclin.B1 Cyclophilin.A Cyclophilin.F Cystatin.C Cystatin.M CYTD CYTF CYTN Cytochrome.c Cytochrome.P450.3A4 CYTT D.dimer DAF DAN DAPK2 DBNL DC.SIGN DcR3 DEAD.box.protein.19B DERM Desmoglein.2 DHH discoidin.domain.receptor.1 Dkk.4 DKK1 DKK3 DLC8 DLL1 DLRB1 DMP1 DnaJ.homolog dopa.decarboxylase DR3 DR6 DRAK2 DRG.1 DRR1 DSC3 Dtk DUS3 Dynactin.subunit.2 ECM1 EDA EDAR EF.1.beta EG.VEGF eIF.5 eIF.5A.1 Elafin Elastase EMAP.2 EMR2 Endocan Endoglin Endostatin Endothelin.converting.enzyme.1 ENPP7 Enterokinase ENTP5 Eotaxin EP15R EphA1 EPHA3 EphA5 EPHAA EPHB2 EphB6 Ephrin.A4 Ephrin.A5 Ephrin.B3 EPI Epithelial.cell.kinase EPO.R ER ERBB1 ERBB2 ERBB3 ERBB4 ERK.1 ESAM Esterase.D ETHE1 FABP FABPE Factor.B Factor.I FAK1 FAM107B Fas.ligand.soluble FCAR FCG2A.B FCG3B FCGR1 FCN1 FCN2 FCRL3 FER Ferritin FETUB FGF.10 FGF.12 FGF.16 FGF.18 FGF.19 FGF.20 FGF.4 FGF.5 FGF.6 FGF.8A FGF7 FGFR.2 FGFR.3 FGFR4 FGR Fibrinogen Fibrinogen.g.chain.dimer Ficolin.3 FLRT1 Flt.3 Flt3.ligand FN1.4 Fractalkine.CX3CL.1 FSH FST FSTL3 Fucosyltransferase.3 FUT5 FYN G.CSF.R GA733.1.protein Galectin.2 Galectin.3 Galectin.4 Galectin.8 GAPDH.liver GASP.2 GCKR GCP.2 GDF.11 GDF.9 GDF2 Gelsolin GFAP GFRa.1 GFRa.2 GIB GIIE GITR Glucagon Glucocorticoid.receptor Glutamate.carboxypeptidase Glutathione.S.transferase.Pi Glypican.3 GM.CSF GNS GOT1 gp130.soluble GP1BA GPC2 GPC5 gpIIbIIIa GPNMB GPVI Granulysin granzyme.A Granzyme.B Granzyme.H GRN Gro.a Gro.b.g Growth.hormone.receptor GSK.3.alpha.beta GV H6ST1 HAI.1 Haptoglobin.Mixed.Type Hat1 HCC.1 HCC.4 HCG HCK HDAC8 HDGR2 Hemopexin Heparin.cofactor.II HGF HGFA HINT1 HIPK3 Histone.H1.2 Histone.H2A.z HIV.2.Rev HMG.1 HMGR hnRNP.A.B hnRNP.A2.B1 hnRNP.K HNRPQ HO.2 HPG. HPLN1 HPV.E7.Type.16 HRG HSP.40 HSP.60 HSP.70 HSP.90a.b HTRA2 HVEM I.309 I.TAC ICOS IDE IDS IDUA IF4A3 IFN.g IFN.g.R1 IFN.lambda.1 IFN.lambda.2 IgD IgE IGF.I IGF.I.sR IGF.II.receptor IGFBP.1 IGFBP.2 IGFBP.3 IGFBP.4 IGFBP.5 IGFBP.6 IGFBP.7 IgG IgM IL.1.R.AcP IL.1.R4 IL.1.sRI IL.10 IL.11 IL.11.RA IL.12.Rb1 IL.12.RB2 IL.13 IL.13.Ra1 IL.15.Ra IL.16 IL.17 IL.17.RC IL.17.RD IL.17.sR IL.17B IL.17B.R IL.17D IL.17E IL.17F IL.18.BPa IL.18.Ra IL.19 IL.1a IL.1F7 IL.1Rrp2 IL.2 IL.2.sRa IL.2.sRg IL.20 IL.20.Ra IL.22 IL.22BP IL.23 IL.23.R IL.27 IL.3 IL.3.Ra IL.34 IL.4 IL.4.sR IL.5.Ra IL.6 IL.6.sRa IL.7 IL.7.Ra IL.8 IL22RA1 IL24 ILT.2 ILT.4 IMB1 IMDH1 IMDH2 ING1 Insulin Integrin.a1b1 Integrin.aVb5 IP.10 IR ITI.heavy.chain.H4 JAG1 JAG2 JAK2 JAM.B JAM.C JAML1 JNK2 K.ras Kallikrein.11 kallikrein.12 kallikrein.13 Kallikrein.4 kallikrein.5 Kallikrein.6 Kallikrein.7 kallikrein.8 Kallistatin Karyopherin.a2 Keratin.18 KI2L4 KI3L2 KI3S1 KIF23 Kininogen.HMW KIRR3 KLRF1 KPCI KPCT KREM2 Ku70 KYNU Lactoferrin LAG.1 LAG.3 Lamin.B1 Laminin Layilin LBP LCK LCMT1 LD78.beta LDH.H.1 LEAP.1 Leptin LG3BP LIF.sR LIGHT LIMP.II LIN7B Lipocalin.2 LKHA4 LRIG3 LRP8 LRRT1 LRRT3 LSAMP Luteinizing.hormone LY86 LY9 Lymphotactin Lymphotoxin.a1.b2 Lymphotoxin.a2.b1 Lymphotoxin.b.R LYN LYNB Lysozyme LYVE1 M2.PK Macrophage.mannose.receptor MAPK14 MAPK2 MAPK5 MAPKAPK3 MASP3 MATK MATN2 MBD4 MBL MCP.1 MCP.3 MCP.4 MDC MDHC MDM2 MED.1 MEPE Mesothelin Met METAP1 MFGM MFRP MIA MICA MICB Midkine MIF MIP.1a MIP.3a MIP.3b MIP.5 MIS MK01 MK08 MK11 MK12 MK13 MMP.1 MMP.10 MMP.12 MMP.13 MMP.14 MMP.2 MMP.3 MMP.7 MMP.8 MMP.9 Mn.SOD MO2R1 Moesin MP2K2 MP2K4 MPIF.1 MRC2 MSP Myeloperoxidase Myoglobin Myokinase.human NACA NADPH.P450.Oxidoreductase NAGK NANOG NAP.2 NCAM.120 NCAM.L1 NCC27 NCK1 NDP.kinase.B Nectin.like.protein.2 NET4 Neurotrophin.3 Neurotrophin.5 NG36 NID2 Nidogen NKG2D NKp30 NKp46 NLGNX NMT1 Nogo.Receptor NOTC2 Notch.1 Notch.3 NovH NPS.PLA2 Nr.CAM NR1D1 NRP1 NRX1B NRX3B NSF1C NUDC3 NXPH1 OCAD1 Olfactomedin.4 OLR1 OMD ON OPG OX2G P.Selectin p27Kip1 PA2G4 PACAP.38 PAFAH PAFAH.beta.subunit PAI.1 PAK3 PAK7 PAPP.A paraoxonase.1 PARC PARK7 PBEF PCNA PCSK7 PCSK9 PD.L2 PDE11 PDE3A PDE5A PDE7A PDGF.AA PDGF.BB PDGF.CC PDGF.Rb PDK1 PDPK1 Periostin PERL Peroxiredoxin.1 Peroxiredoxin.6 Persephin PESC PF.4 PFD5 PGCB PGP9.5 PGRP.S PH PHI phosphoglycerate.kinase.1 Phosphoglycerate.mutase.1 PIGR PIK3CA.PIK3R1 PKB.a.b.g PKC.A PKC.B.II PKC.Z Plasmin Plasminogen PLCG1 PlGF PLK.1 PLPP PLXC1 PPAC PPase PPID PPIE Prekallikrein PRKACA PRL Prolactin.Receptor prostatic.binding.protein Protease.nexin.I Protein.C Protein.disulfide.isomerase Protein.disulfide.isomerase.A3 Proteinase.3 PSA PSA.ACT PSA1 PSA2 PSA6 PSD7 PSMA PSME1 PSME3 pTEN PTH PTHrP PTK6 PTN PTP.1B PTP.1C PYY Rab.GDP.dissociation.inhibitor.beta RAC1 RAD51 RAN RANTES RAP Rb RBM39 RBP RELT Renin resistin RET RGM.C RGMA RGMB ROBO2 ROBO3 ROR1 RPS6KA3 RS3 RS7 RSK.like.protein.kinase RSPO2 RTN4 RUXF SAA SAP SARP.2 SBDS sCD163 sCD4 SCF.sR SCGF.alpha SCGF.beta SDF.1 sE.Selectin SE6L2 Secretin Semaphorin.3A Semaphorin.3E Semaphorin.6A SEPR SET sFRP.3 SGTA SH21A SHBG SHC1 SHP.2 Sialoadhesin sICAM.1 sICAM.2 sICAM.5 SIG14 Siglec.3 Siglec.6 Siglec.7 Siglec.9 SKP1 sL.Selectin SLAF6 SLAF7 sLeptin.R SLIK5 SLPI SNAA SOD Somatostatin.28 Sonic.Hedgehog SORC2 Sorting.nexin.4 SP.D SPARCL1 Sphingosine.kinase.1 SPHK2 SPINT2 Spondin.1 SPTA2 sRAGE sRANKL SRCN1 SREC.I SREC.II SSRP1 ST4S6 Stanniocalcin.1 sTie.1 sTie.2 STRATIFIN Stress.induced.phosphoprotein.1 STX1a suPAR TAFI TAJ TAK1.TAB1 TARC tau TBK1 TBP TCCR TCPTP TCTP TEC TECK Tenascin Testican.1 Testican.2 TF TFF3 TFPI TGF.b.R.II TGF.b.R.III TGF.b1 TGF.b2 Thrombin Thrombopoietin.Receptor Thrombospondin.1 Thymidine.kinase Thyroglobulin Thyroxine.Binding.Globulin TIG2 TIMD3 TIMP.1 TIMP.2 TIMP.3 TLR4.MD.2.complex TNF.a TNF.b TNF.sR.I TNF.sR.II TNFSF15 TNFSF18 TNR4 Topoisomerase.I tPA TPSB2 TPSG1 TRAIL.R4 transcription.factor.MLR1.isoform.CRA_b Transketolase TrATPase Triosephosphate.isomerase TrkA TrkB TrkC Tropomyosin.1.alpha.chain Tropomyosin.2 TRY3 Trypsin Trypsin.2 TS TSG.6 TSH TSLP TSLP.R TSP2 TSP4 TWEAK TWEAKR TXD12 UB2L3 UBC9 UBE2N Ubiquitin.1 UFC1 UFM1 ULBP.1 ULBP.2 ULBP.3 UNC5H3 UNC5H4 uPA URB Vasoactive.Intestinal.Peptide VCAM.1 VEGF VEGF.C VEGF.sR2 VEGF.sR3 VEGF121 WFKN2 WIF.1 WNT7A X14.3.3 X3HIDH X4.1BB X4.1BB.ligand X41 X4EBP2 X6.Phosphogluconate.dehydrogenase X6Ckine XEDAR XPNPEP1 XTP3A YES ZAP70 Fast.Progression
100080 142 141 2/17/2015 5/3/2010 0 1751 57.4 68 64 9 1946 Female 1358 PD 2.770263 3.352511 3.179236 3.846733 2.303628 2.814714 2.601408 3.090399 3.901742 2.547036 3.290235 2.760498 2.581722 2.564903 3.822332 4.593109 3.462503 2.955592 3.459784 3.827369 3.574818 4.030539 4.076972 3.370032 4.949672 3.610894 5.152935 3.560648 2.755418 2.736237 3.491558 4.371088 4.057202 2.190892 3.609370 2.638888 2.824972 2.610554 3.958975 3.257294 2.857031 3.409104 4.370260 3.925771 3.974576 4.869780 4.172810 4.097431 3.834096 4.209459 5.140156 5.007874 3.451648 2.987175 2.847141 3.522744 2.894150 3.010893 3.300943 2.846832 5.144554 2.827369 3.568331 3.822835 2.479287 3.759434 2.405005 3.540430 2.580925 3.033946 3.360896 2.524266 2.528274 2.769968 2.616370 3.015234 2.891035 3.248954 2.967267 3.043559 3.904445 3.242517 3.193403 3.103735 3.374070 2.839478 3.078855 3.083646 4.488689 3.716195 3.442339 3.142765 2.943742 2.746556 2.419129 2.692583 2.749350 2.574031 3.205231 3.459030 3.222222 3.310205 3.136562 3.836153 3.729675 4.168830 3.123362 3.909021 3.277701 2.976029 2.694078 3.818094 4.179489 2.791410 3.253023 3.622618 4.851012 3.297957 3.731323 4.714093 3.294554 4.162352 4.480692 4.077470 3.014226 3.437053 3.713575 2.431203 3.257271 2.775902 2.863323 4.610004 2.416141 4.099605 3.369828 2.179264 3.022181 3.346373 3.536748 2.565021 3.880282 4.362795 2.221153 3.619125 3.091210 3.363950 3.401556 2.816573 3.317332 3.464326 3.346979 3.697474 3.701637 3.130912 3.048752 3.677333 3.343901 4.305186 2.390759 2.743039 3.435956 3.358239 3.340860 2.537945 2.656002 2.677151 3.232335 3.242715 3.768749 3.196618 3.065131 2.255031 3.968086 2.399501 2.294025 4.738297 2.805161 3.407646 3.263068 4.071584 2.845842 4.211854 2.235528 2.659060 3.487350 4.437007 2.551328 4.101163 3.084004 2.696968 4.188174 4.011012 2.937217 4.108054 3.440358 4.156637 3.880276 3.476034 2.885474 2.578066 2.694166 3.751818 2.789016 3.270981 3.484072 3.543373 2.611829 4.048888 4.073902 2.797752 3.211974 3.732088 2.842360 3.191647 4.130073 3.419295 2.322426 4.095699 2.582063 5.020362 3.988728 3.340543 3.730572 3.565293 2.787744 3.114044 3.286838 4.277114 4.105922 4.192629 4.184652 3.129497 3.515384 3.238071 3.416607 2.855943 3.019324 3.354051 4.786744 2.957176 2.406370 3.702784 4.049233 3.693120 2.784974 3.155852 3.774765 2.477700 2.763578 2.636488 3.972712 3.878826 4.177202 4.490786 2.601625 2.638090 3.164353 3.357801 2.671173 4.401039 3.315529 3.007065 3.185117 2.525692 3.398600 3.966123 3.740339 2.658870 3.715853 2.525045 3.016783 3.298657 4.686290 4.284054 3.435908 2.263636 3.685643 4.053708 2.668759 3.875698 2.156246 2.944779 2.590173 4.140153 3.259880 3.111498 3.645009 2.845656 3.218352 3.621187 2.439333 3.110825 4.567138 2.502700 2.841735 2.044932 4.485311 3.391464 4.115058 3.508018 3.452645 2.871456 4.338638 4.780559 2.294025 3.424669 2.810837 2.247237 3.615950 3.158061 2.411788 3.902601 4.010978 2.777499 2.938820 2.931153 3.706846 2.310056 2.353916 3.332075 2.560146 3.924450 2.700098 2.384353 3.208334 2.732715 2.370328 3.154880 2.792672 2.721646 3.017576 2.695307 5.133902 4.832668 3.140319 2.903578 3.614887 2.891259 4.724772 3.791248 3.917006 2.912860 3.825433 2.634377 3.899656 3.748638 2.849296 2.678609 2.767898 4.104070 3.101541 2.565376 4.844418 2.760875 2.757244 3.395955 2.763653 3.097604 3.269583 3.166134 2.828853 3.187605 3.906615 2.862310 2.369030 2.771955 3.246843 1.971276 3.089693 3.974480 3.039335 4.101396 3.558301 3.845575 4.054307 3.004020 2.375846 2.839227 4.073311 3.996936 3.743690 3.263944 3.385141 2.271842 2.440594 4.436810 3.496362 2.866169 2.980549 4.023248 2.575419 2.407051 4.021913 3.856312 2.409933 3.983207 4.249223 3.875287 2.276232 3.458063 2.854245 2.631849 3.244500 2.956649 3.725430 3.596916 2.924124 3.317436 3.307475 2.219060 4.661780 2.572872 3.649179 4.129151 2.368844 3.423704 2.623559 3.436401 3.057666 2.144574 3.381205 2.673113 4.027778 4.007167 4.498002 3.603188 2.638888 3.662437 3.415040 2.856729 3.667322 3.727004 2.938720 2.781109 2.955351 2.656098 2.468052 2.255996 2.695044 3.796484 3.190864 3.932930 4.531996 2.724522 4.709845 3.144574 3.957933 3.293583 2.945075 4.659200 4.463901 3.437069 4.850639 3.221727 3.745535 2.608954 2.419956 3.885819 2.534153 2.960090 2.903144 3.564974 3.028531 3.681648 2.287130 3.020734 2.697055 2.522183 2.423082 3.316934 2.617839 2.672652 2.994757 3.886959 3.920869 3.968511 2.948071 3.200194 2.985157 3.255272 2.960566 3.528029 2.661623 2.591176 3.048481 3.483758 2.434888 3.303606 3.045597 3.208334 2.809829 2.919706 2.603144 2.586587 3.044775 2.512951 4.364224 2.449478 2.626443 3.099853 2.749659 2.372175 3.581563 3.986906 4.082214 3.600068 3.527037 3.918251 2.085647 3.017117 3.102125 3.231826 3.988728 4.360896 3.197501 2.683227 4.193345 3.778310 3.505638 2.712902 2.609701 2.983987 2.871573 3.794132 3.208226 2.503246 2.550106 3.609904 2.973313 3.467194 4.745446 2.671543 2.575765 3.337619 3.504770 2.668293 2.955495 4.560745 2.521922 2.619615 3.394417 3.431010 3.333890 2.569491 2.831742 4.760028 2.759668 4.165188 2.882809 3.242790 3.244129 4.240067 2.749968 3.537806 3.452109 3.266631 3.876720 3.876247 2.748808 2.423737 2.745465 2.557387 2.622421 3.675201 2.956409 3.966461 3.389875 1.987219 2.167317 3.243460 3.645501 3.767712 3.576099 2.670060 2.311966 3.448165 3.846547 3.716529 4.417447 4.544933 2.696793 4.320090 3.730782 3.121790 3.765236 3.182243 3.096215 3.758632 2.402261 4.180278 2.909663 3.992297 2.741545 2.949975 2.717004 3.062507 4.457706 2.415307 2.698449 2.380392 2.810098 4.160454 3.339272 2.860458 2.934700 3.564654 2.604766 2.522575 4.145631 3.178545 3.064720 2.299725 2.811642 3.747777 4.214849 3.971188 3.113241 2.729084 2.866405 3.113208 2.625107 2.557868 3.062544 2.775829 3.029952 3.505638 3.006766 3.093176 3.020982 4.462254 4.658439 3.118959 3.152930 3.673436 4.789697 3.320312 3.320603 3.530993 4.291819 3.455804 4.569796 3.870140 3.402485 2.737272 2.307282 3.423737 4.085080 2.727216 3.657992 2.972110 4.069783 3.388403 2.945469 2.574957 2.307710 3.359038 3.306939 3.415124 2.874772 2.663418 3.440594 2.780749 3.659289 3.244623 2.670524 4.256145 2.966095 2.897187 3.625004 4.258441 2.554731 2.811106 2.970068 3.000521 2.829175 2.746167 4.157910 3.073572 2.251151 3.425387 4.026190 4.185613 4.220474 3.541479 3.757411 3.225516 3.268625 2.739651 3.157698 3.561995 2.575188 3.206772 2.960423 3.877866 2.333246 3.743643 2.998956 3.059033 2.180413 3.333387 3.050766 3.571744 3.341632 3.339173 4.224352 2.553155 3.438590 4.275664 2.686189 4.019926 3.405654 3.301247 3.436910 2.815910 3.427324 2.853211 2.347330 2.617943 4.282572 2.705179 3.094890 3.254113 2.765743 3.437608 4.323089 3.704288 3.036629 3.517037 3.009833 3.509941 4.309394 3.723127 3.000391 2.828789 3.312918 2.790567 2.594613 2.269279 3.536987 2.910838 3.351487 4.525958 3.389644 3.073865 4.735920 3.797766 2.915716 3.357535 3.890605 3.393171 3.252416 2.337260 3.800738 3.276554 2.351603 3.897616 2.905850 2.887842 2.809829 2.623972 3.455925 3.207877 2.489255 2.754654 2.257679 2.970300 2.479143 3.120146 3.613937 3.770557 2.896967 4.434812 4.220477 3.460823 3.573857 3.744011 3.023211 3.170907 2.821645 3.022305 3.071845 3.053885 2.992863 3.036669 3.976762 4.444032 3.271726 3.585709 2.519172 3.061566 3.181043 3.212880 3.655676 2.511883 2.579441 4.912427 4.034580 3.683110 4.593013 3.578261 3.789404 3.414622 2.622318 4.299185 3.336059 2.982904 3.776534 4.620360 3.163966 2.464042 2.885191 3.468834 4.178594 2.985516 2.843793 3.626597 3.284724 3.264936 3.672126 3.930674 3.799113 2.042182 3.757624 2.952744 2.747645 4.206656 3.555143 2.162266 3.054498 4.334116 3.442636 3.812231 2.684127 4.593084 3.248047 3.569210 4.346296 3.828641 3.186221 2.694517 3.128011 2.428459 3.855519 4.738237 4.200100 3.876108 2.685921 3.069261 3.287936 2.250176 2.986234 3.329947 4.277488 3.717338 2.934852 2.848312 3.552157 3.372691 3.992408 3.310884 2.942455 3.920363 2.789369 3.862459 4.329361 3.069520 3.514189 3.123492 2.983626 2.714414 2.894538 3.203414 2.745075 3.996297 3.458895 3.384998 4.318656 3.417787 2.875409 3.153632 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2.751510 4.186917 2.842172 3.336159 3.241297 4.609994 2.636087 3.425306 3.445012 3.309715 3.870064 3.743377 2.716754 2.493876 2.964542 2.638190 2.535168 3.771043 3.082175 3.770543 3.354473 1.951337 2.155336 3.297257 3.419510 3.792076 3.713314 2.617943 2.164353 3.306489 3.744582 3.822756 4.426695 4.521712 2.489114 4.522148 3.789954 3.182985 3.907352 3.140225 3.030316 3.610117 2.645717 4.194314 2.960138 3.507829 3.345922 3.075839 2.586024 3.242268 4.466245 2.408070 2.703807 2.481156 2.639984 3.943059 3.546296 2.930134 3.039731 3.784639 2.319522 2.826852 4.414836 3.345040 3.241820 3.099059 2.936966 3.822253 4.083420 4.070555 3.078203 2.734640 2.957032 2.790144 3.261049 2.627775 3.132548 2.826658 3.057476 3.465829 3.000478 3.271818 3.014647 4.399584 4.582434 3.031287 3.115577 3.569924 4.263139 3.219846 3.318356 3.599501 4.294654 3.520628 4.599842 4.015883 3.394609 3.165066 2.292920 4.007304 3.989058 2.649432 3.861260 3.022964 4.266584 3.305867 3.055684 2.594834 2.314920 3.320686 3.575604 3.524162 2.944137 2.668945 3.484926 2.901404 3.592154 3.107956 2.906227 4.145125 2.951483 2.823735 3.663079 4.211008 2.651084 2.874656 2.909235 3.037745 2.927883 2.751664 4.130803 3.071772 2.174932 3.422508 4.040950 4.588181 4.054961 3.479114 3.918413 2.861056 3.300530 2.856850 3.116973 3.632619 3.255272 3.100818 2.981003 4.293928 2.218273 4.081768 2.807603 3.063033 2.135769 3.357058 3.026737 3.556049 3.396182 3.301681 4.243829 2.615529 4.171317 4.915594 2.736874 4.129206 3.488156 3.277036 3.584862 2.845160 3.620563 3.085326 2.297979 2.719580 5.118152 2.622835 2.940616 3.403241 2.943692 3.173594 4.320128 3.785750 3.255104 3.591098 3.064608 3.621166 4.392644 4.021561 3.056142 2.804616 3.336320 2.580697 2.693639 2.306639 3.580765 2.918554 3.884563 4.401126 3.671737 3.213597 4.460010 3.709948 3.201233 3.236235 3.969467 3.400072 3.152227 2.337659 3.902351 3.406370 2.267406 3.861230 3.063033 2.776774 2.871865 2.573336 3.365357 3.148140 2.675503 3.240624 2.387568 2.975386 3.048791 3.256549 3.462533 3.779762 3.123819 4.477386 4.346181 3.381620 3.700868 4.222355 3.043794 3.185004 2.567262 3.100784 3.158694 2.976762 3.206259 2.914396 3.880682 4.313639 3.295391 3.602440 2.512818 3.124830 3.162505 2.796644 3.229426 2.519040 2.634175 4.741660 3.427892 2.820792 4.466442 3.856608 3.897165 3.621239 2.630936 4.325805 3.200303 2.869642 3.847357 4.332143 3.260215 2.424718 2.786183 3.418135 4.042245 2.877198 2.788875 3.602873 3.345256 2.870053 3.775778 4.002516 3.813261 1.993436 3.699768 2.904770 2.926445 3.960047 3.586711 2.418964 3.089623 4.520169 3.488734 3.792728 2.820858 4.861601 3.365806 3.506234 4.430693 4.005494 3.211734 2.777354 2.948706 2.307282 4.009948 4.292517 4.214221 4.029688 2.722881 3.283753 3.565718 2.468790 2.938119 3.262309 4.345682 3.837891 2.829304 2.687707 3.588944 3.480539 3.979771 3.170965 3.211414 4.104364 2.674769 3.893012 4.213621 3.213226 3.345080 3.585416 3.108328 2.739810 2.706035 3.119388 2.974696 4.080569 3.657400 3.360574 4.362390 3.401125 2.807400 3.203441 3.878464 4.676714 3.139281 4.538699 3.356332 2.728759 3.412158 2.513750 3.675192 2.826593 2.785970 4.674288 3.583947 3.887358 2.666424 2.842484 3.685034 3.561817 2.712566 2.860098 3.181071 4.042906 3.433738 2.824516 3.032296 3.043559 2.615950 3.551670 2.459091 5.084829 2.248709 4.680967 3.805242 4.000126 3.314183 2.762604 3.453594 3.024198 3.338576 2.662947 3.775108 3.040919 2.827498 3.156701 3.664266 2.470851 3.396513 4.204641 2.986593 2.494155 2.576687 3.635403 3.350539 3.444263 3.832835 4.249017 4.297690 3.704811 2.816241 2.390759 3.048286 3.656664 3.781080 3.539490 3.644439 2.643847 3.880734 4.185267 3.089340 3.708905 3.568554 2.866642 3.763053 3.691833 2.565494 4.032401 2.832892 2.528274 2.368844 4.418492 2.712060 4.232073 3.826133 3.282395 4.056558 4.329113 3.102056 3.735775 1
101690 140 139 9/14/2016 10/5/2011 0 1806 59.2 68 63 8 1948 Male 1964 PD 2.871164 3.251663 3.144854 3.934251 2.265996 2.670895 2.631647 2.833147 3.824523 2.521138 3.637810 2.780101 2.660486 2.746790 3.575465 4.606060 3.486388 2.865045 3.406046 3.769400 3.474537 4.088721 4.109784 3.386838 5.080719 3.608932 5.068577 3.865856 2.686368 2.884115 3.481686 4.383086 4.156364 2.093422 3.706453 2.743980 2.824841 2.697142 4.164317 3.156004 2.840106 3.380392 3.985952 3.813147 3.758503 4.789092 4.161149 4.267059 3.863132 4.335811 5.214506 5.093184 3.431106 3.063596 2.726483 3.578330 2.876333 3.180212 3.254959 2.836641 4.963150 3.326827 3.543596 4.047349 2.477844 3.697282 2.407391 3.592421 2.570776 3.113910 3.585156 2.516271 2.527114 3.085362 2.779452 2.869232 2.990871 2.989939 3.106667 3.096284 3.692459 3.412813 3.212161 3.144916 3.610543 2.848989 3.471614 3.175541 4.677889 3.751402 3.570298 3.430623 3.232996 2.842796 3.023623 2.821120 2.795463 2.666986 3.380591 3.558709 2.953760 3.434968 3.151125 3.648467 3.704082 4.139760 3.135927 3.841585 3.379922 2.898396 2.667640 3.851430 4.135517 2.826787 3.280260 3.790194 4.871370 3.264676 3.912801 4.800869 3.254572 3.847949 4.433703 4.058506 2.907680 3.260596 3.590061 2.388634 2.975340 3.852529 2.911690 4.600062 2.346157 3.740228 3.493486 2.252368 3.108328 3.349433 3.520156 2.523096 3.944936 4.292278 2.332438 3.828731 3.249247 3.326745 3.431444 2.910090 3.750045 3.522757 3.413384 3.683182 3.889655 3.171522 3.040879 3.835120 3.414890 4.185389 2.347135 2.854367 3.074194 3.324241 3.454951 2.623042 2.693727 2.747800 3.322550 3.240275 3.769680 3.222378 3.565352 2.313867 3.986485 2.439175 2.381115 4.963861 2.950900 3.151063 2.946894 4.000577 2.891314 4.170526 2.290702 2.684037 3.559512 4.521873 2.613736 4.039386 2.898835 2.699664 4.201834 4.044798 2.751125 4.108680 3.354800 3.987090 4.057248 3.408732 2.866996 2.630733 2.725094 3.820865 2.663795 2.830011 3.473020 3.587890 2.562174 3.983247 4.235170 2.796297 3.252270 3.623849 2.841547 3.168821 4.098436 3.368361 2.305136 4.082135 2.560146 4.951277 3.948066 3.293473 3.857953 4.160622 2.902329 3.336500 3.396949 4.304742 4.303358 4.211014 4.125582 3.065281 3.459573 3.195844 3.215056 2.703291 3.033102 3.348149 4.748500 2.931407 2.480151 3.533314 3.957665 3.785166 2.729084 3.179437 3.619250 2.586475 2.791199 2.617629 3.915563 3.825192 3.804017 4.407041 2.698883 2.406881 3.158543 3.306768 2.692847 4.287208 3.366255 3.000260 3.148140 2.552547 3.470793 3.908705 3.963486 2.691524 3.761161 2.673482 3.162773 3.398339 4.685892 4.392116 3.990308 2.323458 3.808008 4.324023 2.655619 3.621685 2.055760 2.831550 2.642069 4.070861 3.197143 3.120673 3.631007 2.854063 2.991802 3.444482 2.453012 2.996555 4.578144 2.980185 2.789510 2.065206 4.302201 3.373537 3.980590 3.578410 3.279142 3.301008 4.413826 4.738506 2.445448 3.435191 3.075218 2.270213 3.356924 2.989628 2.415641 3.901469 3.584908 2.861355 2.864630 3.065057 3.569526 2.389166 2.223756 3.283437 2.483587 4.017655 2.739730 2.448088 2.571359 2.715335 2.331630 3.096354 2.928191 2.849726 3.182329 2.746089 5.103294 4.869477 2.940915 2.880185 3.643985 2.845904 4.884236 3.847060 3.044265 2.939918 3.784453 3.035790 4.377639 3.705889 2.795741 2.683857 2.731024 4.148041 3.093387 2.705949 4.840756 3.052656 2.948071 3.192233 2.864036 2.922154 3.312410 3.137987 2.814780 3.221701 3.935316 2.886604 2.510277 2.891816 3.451955 1.979093 3.075729 3.859691 3.138429 2.770705 3.629002 3.607637 4.153385 3.074524 2.408918 2.883548 4.049962 4.041278 3.702723 3.324324 3.524824 2.332842 2.491502 4.489062 3.400175 3.515317 2.800305 3.989619 2.570893 2.488692 4.063652 4.448222 2.398287 3.919726 4.658236 3.461949 2.345570 3.460853 2.737113 2.636388 3.340722 3.090928 3.936760 2.642267 3.088526 2.463594 3.007748 2.322839 3.907857 2.521792 3.240075 3.428669 2.433450 3.470513 2.647969 3.440547 3.287443 2.144574 3.357210 2.694868 4.011676 3.953068 4.330511 3.612413 2.656098 3.807792 3.414255 2.844415 3.774517 3.804078 3.166430 2.757168 2.916349 2.804480 2.332236 2.302547 3.345668 2.969788 3.093597 4.153025 4.595982 2.626853 4.845984 2.941958 3.776338 3.116276 2.917243 4.728206 4.266958 3.345374 4.885228 3.748188 3.838931 2.659631 2.411283 3.971865 2.603686 2.947630 2.984752 3.591654 3.053616 3.292234 2.353532 3.161937 2.659155 2.854610 2.466868 3.648692 2.621488 2.714916 3.100612 3.938750 4.153425 4.141585 3.203686 3.311521 3.072911 3.394837 2.911477 3.507221 2.721068 2.481156 3.087817 3.599140 2.475526 3.298154 3.035990 3.206961 2.847573 2.996687 2.664360 2.604766 3.487294 2.582745 4.357943 2.295787 2.790496 3.062206 2.740284 2.342620 3.647451 4.069254 4.015540 3.384371 3.478292 3.858970 2.282622 3.017326 2.971137 3.256742 4.178617 4.462664 3.237041 3.007150 4.338646 3.768764 3.435111 2.842796 2.758761 3.042969 3.080374 4.077382 3.272723 2.571010 2.530456 3.695972 3.041037 3.418898 4.756171 2.751741 2.586137 3.144543 2.528274 2.700011 2.828531 4.695771 2.636187 2.666892 3.340464 3.362652 3.360745 2.049218 2.909930 4.929021 2.655427 4.020776 2.819149 3.365076 3.200932 4.486974 2.719414 3.377270 3.546000 3.044304 4.098270 4.358833 2.828402 2.501059 2.740678 2.626648 2.590396 3.770329 3.039097 4.026693 3.480711 2.054996 2.228913 3.306554 2.929879 3.754616 3.410575 2.587150 2.305136 3.484385 3.947149 3.669233 4.396991 4.661453 2.498999 4.380294 3.756864 3.051461 4.011147 2.844166 2.844912 3.754096 2.442636 4.125709 3.148603 4.022028 2.678791 2.918973 2.483872 3.124178 4.369019 2.430559 2.933133 2.428135 2.639586 4.204435 3.459739 3.094226 3.604701 3.665506 2.408410 2.878637 4.212097 3.156216 3.089304 2.256718 2.885361 3.527797 4.207209 3.932773 2.973082 2.753430 2.803730 2.708761 2.960233 2.778730 3.307881 2.708846 3.052502 3.554186 4.038394 3.240400 3.005995 4.347002 4.643579 3.230423 3.044853 3.431557 4.562242 3.474901 3.215796 3.893790 4.454528 3.365582 4.600471 3.799630 3.738574 3.012922 2.383277 3.428281 4.204923 2.864689 3.657075 3.128335 4.174426 3.387941 3.000608 2.687529 2.396896 3.293694 3.403755 3.603815 2.918030 2.735679 3.416774 2.966986 3.537668 3.297848 2.805433 4.326274 2.945911 2.898670 3.520090 4.474034 2.579212 2.854185 2.942603 3.048131 2.773347 2.626238 3.904981 2.995942 2.180699 3.364907 4.054291 4.103547 4.386654 3.598188 3.969015 2.871398 3.284949 2.779524 3.178718 3.609392 2.574610 3.332337 2.965860 4.279840 2.288025 3.684271 2.778658 3.024486 2.185825 3.568378 3.095065 3.613123 3.436163 3.375719 4.170227 2.557026 3.441617 4.105721 2.744684 4.157496 3.723135 3.136689 3.529802 2.848066 3.479446 2.830011 2.419129 2.539829 4.052998 2.702689 3.117570 3.256862 2.856124 3.708106 4.191557 3.792125 3.000000 3.639357 3.042615 3.476904 4.369774 3.717238 2.877544 2.791971 3.456290 2.714832 2.853698 3.213597 3.467119 2.842796 3.435908 4.433418 3.387941 3.096354 4.640129 3.718676 2.865222 3.322343 3.819939 3.211788 3.366460 2.437275 3.807948 3.318210 2.710456 3.827550 2.908967 2.835310 2.794976 2.508664 3.557880 3.105101 2.547405 2.678973 2.356599 2.966470 2.456821 3.093877 3.575892 3.496071 3.011401 4.381176 4.243251 3.632842 3.655993 3.805929 3.130977 3.443263 2.387746 2.963316 3.102502 2.985067 3.024609 3.061716 4.046437 4.492122 3.251273 3.652343 2.582745 3.059488 3.027553 2.647676 2.902003 2.436481 2.530840 4.629696 3.512377 3.542527 4.675950 3.600472 3.768372 3.535497 2.655619 4.145796 3.409747 3.066810 3.786915 4.527924 3.082857 2.458033 3.057628 3.429817 4.301037 2.937668 2.741860 3.794662 3.244104 2.745699 3.882889 4.212014 3.633196 2.097951 3.822312 3.001734 2.967361 4.152328 3.371696 2.191171 3.251541 4.668666 3.398253 3.775742 2.746945 4.556715 3.591932 3.553082 4.491549 3.778679 3.109579 2.739493 3.460642 2.598900 3.818958 3.720754 4.311173 3.851387 2.680517 3.097535 3.389609 2.239800 2.909556 3.453012 4.173591 3.687761 2.793301 2.589167 3.633155 3.486601 4.042808 3.402313 2.915189 3.912525 2.744528 3.819583 4.368963 3.120376 3.361010 3.040642 3.046339 2.714581 2.674034 3.376650 2.714581 3.936473 3.496071 3.397853 4.511965 3.351062 3.018368 3.187887 3.806445 4.923693 3.268812 4.449650 3.268742 2.431203 3.148479 2.576917 2.854488 2.960376 2.301030 4.728374 3.546456 3.911360 2.429591 2.747334 2.898176 3.818773 2.649237 2.698622 3.036629 3.949507 3.357344 2.849788 2.601951 3.554586 2.785116 2.965108 2.475381 5.002537 2.333447 4.615734 4.038529 3.857206 3.213916 2.899930 3.637139 2.973636 3.494530 2.722469 3.848060 2.817632 3.030842 3.233402 2.895533 2.437275 3.468746 4.166874 2.733037 2.394802 2.665769 3.516324 3.226858 3.255031 3.666902 4.179443 4.278802 3.511616 2.920071 2.432969 2.987979 3.597531 3.849548 3.739754 3.373041 2.534153 4.060052 4.177095 2.862131 3.954768 3.783896 2.767304 3.837380 3.570858 2.525692 3.818483 2.819873 2.592510 2.802500 4.164757 2.496930 4.089043 3.829715 3.069224 3.841997 4.397365 3.058957 3.948853 0
101943 144 143 2/11/2015 12/8/2010 0 1526 50.0 62 58 4 1952 Female 0 PD 2.711723 3.366180 3.169557 3.804242 2.347915 2.627058 2.692935 2.806655 3.650939 2.473779 3.150941 3.204988 2.611086 2.643749 3.332398 4.626629 3.399933 2.785614 3.454464 3.820694 3.589804 3.920801 4.123064 3.323087 4.943632 3.614802 5.170493 3.866075 2.757927 2.931661 4.215957 4.377497 4.075682 2.224015 3.608258 2.712986 2.841610 2.663889 4.045362 3.189518 2.930694 3.465070 4.240993 3.922476 3.874551 4.751276 3.938985 3.870615 3.869513 4.495665 5.301210 5.191139 3.228144 2.992288 2.688687 3.497814 2.812713 3.098921 3.169880 2.866169 4.919799 2.896692 3.458169 3.882519 2.573104 3.665722 2.554126 3.621103 2.565376 3.220866 3.193347 2.489677 2.478278 2.572872 2.917820 2.979958 3.346588 3.077586 2.944038 3.186080 3.853589 3.390688 3.116674 3.087178 3.657906 2.943346 3.396844 3.019988 4.480204 3.594459 3.544651 3.323623 2.867173 2.859319 2.636087 2.489114 2.771661 2.523877 3.346177 3.384658 3.049334 3.474508 3.120541 3.902585 3.727435 3.815332 3.532130 3.879738 3.311839 3.106327 2.689753 3.862573 4.421795 2.784974 3.219637 3.650463 4.886739 3.331933 3.858411 4.757841 3.101575 4.080616 4.443640 4.021520 3.013511 3.684271 3.690816 2.356026 3.109815 3.363875 2.954821 4.300702 2.260310 3.429155 3.369809 2.615003 3.025674 3.428200 3.522874 2.445604 3.802144 4.110277 2.538699 4.043449 3.181443 3.361180 3.469144 2.894150 3.510679 3.670682 3.350287 3.570263 3.788190 3.118926 3.097951 3.966024 3.453731 4.083685 2.374015 2.859319 3.604528 3.428653 3.346118 2.503655 2.649432 2.727297 3.264700 3.213066 3.799154 3.178401 3.398027 2.282169 3.932149 2.498311 2.374748 4.930010 2.798927 2.949488 2.897352 3.911147 2.829947 4.129448 2.301898 2.981683 3.534280 4.534340 2.582518 4.110687 2.995679 3.019615 4.233897 4.074901 3.009578 4.111262 3.404492 4.141243 3.787361 3.476382 2.979958 2.569725 2.775974 3.848318 2.783189 3.009876 3.399829 3.445137 2.659726 3.924109 4.750628 2.787177 3.188563 3.679646 2.830139 3.117901 4.030345 3.322012 2.468200 4.071396 2.524656 4.963452 3.982610 3.307753 3.571953 3.515225 2.907250 2.598243 3.234618 4.188284 3.616181 4.178991 4.141917 3.055417 3.517037 3.145756 3.310608 2.676968 2.846585 3.409291 4.906396 2.864808 2.624282 3.595739 4.192765 3.501183 2.685383 3.909021 3.544515 2.561101 2.789369 2.569140 4.283936 3.798236 3.941571 4.411576 2.626956 2.461649 3.155457 3.216957 2.588048 4.470400 3.269676 2.934751 3.064608 2.524136 3.320562 3.837948 3.900531 2.681603 3.607401 2.379306 2.886885 3.432937 4.742407 4.384240 3.942920 2.541953 3.671488 4.198368 2.813981 3.847974 2.101747 2.759970 2.504471 4.215048 3.215320 3.128528 3.625601 2.973035 3.320167 3.448552 2.442480 2.996380 4.546317 2.589167 2.836577 2.162863 4.458420 3.376504 3.769333 3.460206 3.350248 3.044108 4.311383 4.878965 2.310481 3.430188 2.893873 2.255272 3.387283 3.392697 2.440909 3.693876 3.810125 2.978272 3.008515 2.769377 3.409595 2.322426 2.219846 3.165333 2.455910 3.207338 2.659726 2.397592 2.974880 2.960994 2.300161 3.105408 2.528917 2.785259 3.173274 2.694693 5.124318 4.826887 3.099197 2.865933 3.588988 2.830845 4.787311 3.759849 3.828215 3.025634 3.831159 2.596377 3.812967 3.744418 2.917978 2.579669 2.850830 4.084826 3.054843 2.594945 4.824316 3.018950 2.877717 3.216957 2.779091 2.814114 3.355222 3.071366 2.775902 3.319585 4.025560 2.837715 2.389343 2.807941 3.086609 1.909021 3.107108 3.738852 2.924021 2.660581 3.557098 3.788861 4.077847 3.003977 2.426349 2.763353 4.080904 3.969728 3.711967 3.499165 3.687315 2.297323 2.494433 4.478435 3.535522 3.387888 3.198300 4.015511 2.563481 2.465383 4.066561 4.335348 2.305566 3.924062 4.706188 4.155907 2.388634 3.419361 2.746401 2.806112 3.224507 3.021727 3.694131 2.590396 2.923607 2.946010 3.117205 2.235781 4.288759 2.516535 3.318481 3.679346 2.422426 3.623229 2.539327 3.352742 4.349668 2.129045 3.306146 2.661150 5.011144 3.949731 4.448009 3.540317 2.662475 3.687627 3.807893 2.768564 3.713281 3.710270 3.213757 2.637089 2.813114 2.705351 2.286905 2.383636 3.911562 2.781612 3.113910 3.866884 4.488672 2.592066 4.474428 3.054996 4.028099 3.193375 2.895367 4.702224 4.343229 3.272978 4.596057 3.379197 3.787744 2.640978 2.428135 3.962497 2.644832 3.040840 3.006509 3.415741 3.053040 3.521635 2.344981 3.065654 2.681874 2.683857 2.429591 2.992421 2.816374 2.743118 3.022676 3.993683 4.071307 3.864469 2.787106 3.218929 3.002080 3.346470 3.057095 3.365488 2.736636 2.700963 3.026615 3.689584 2.533772 3.261001 3.212534 3.243038 2.744762 2.941859 2.610873 2.696269 3.249541 2.700357 4.152866 2.669875 2.611936 3.023088 2.774663 2.387746 3.369587 4.092987 3.980571 3.509915 3.408783 3.816699 2.130655 3.180183 2.960566 3.469115 3.963013 4.299895 3.200331 2.771146 4.122560 3.782207 3.322984 2.786325 2.626340 3.006466 2.913708 3.776047 3.203060 2.575419 2.566320 3.632832 3.047820 3.477888 4.746868 2.640978 2.610341 3.107719 2.645913 2.684756 2.796782 4.678061 2.490239 2.673205 3.364307 3.371437 3.273302 3.040088 3.207823 4.759091 3.010257 4.257712 2.857694 3.203441 3.162744 4.326063 2.730136 3.419162 3.484229 3.168792 3.475540 4.013469 2.866405 2.856789 2.779524 2.723948 2.619928 3.751379 2.995898 3.964919 3.347486 2.320977 2.368659 3.309971 3.659098 3.748963 3.551865 2.644143 2.376942 3.479172 3.842965 3.746276 4.407399 4.564066 2.463893 4.337265 3.699170 3.090928 3.764027 2.959852 2.812312 3.762086 2.522575 4.171981 2.837652 3.754371 2.778224 2.922414 2.798789 3.150787 4.300063 2.422754 2.613842 2.622421 2.592288 4.021962 3.422787 2.772468 2.895423 3.589425 2.386499 2.821055 4.141544 3.228169 3.113576 2.283527 2.919914 3.437132 4.187109 3.948990 2.930083 2.785116 2.789299 2.872040 2.496099 2.604982 2.869994 2.845656 3.001084 3.481098 3.035990 3.037347 3.169027 4.266321 4.598392 3.114511 3.125611 3.482001 4.705682 3.475860 3.409696 3.558252 4.419928 3.440468 4.360862 3.733358 3.577549 3.022346 2.379306 3.371696 3.956072 2.625004 3.525213 3.145973 4.097566 3.377598 3.095692 2.570543 2.357554 3.289901 3.228015 3.270749 2.891370 2.698709 3.497676 2.825361 3.532002 3.366964 2.743353 4.272939 2.948853 2.926857 3.747629 4.285850 2.598243 2.861415 2.834866 3.086395 2.778079 2.641077 4.068627 3.082642 2.260310 3.388687 4.166679 4.291786 4.178531 3.523343 3.807839 2.946501 3.212933 2.759366 3.072948 3.411333 2.829754 3.218010 3.649481 3.872820 2.314499 3.852212 2.765296 2.980821 2.155943 3.620979 3.049993 3.587823 3.314183 3.386713 4.099242 2.591955 3.505869 4.226303 2.585799 3.617504 3.408969 3.276025 3.434345 2.873088 3.308458 2.832126 2.350442 2.811441 4.181366 2.672190 2.920123 3.176381 2.717254 3.346881 4.240764 3.762634 3.864766 3.623725 3.034107 3.471277 4.230474 3.738091 2.918240 2.890812 3.367431 2.799409 2.632356 2.640481 3.359399 2.974880 3.247900 4.279316 3.566508 2.907304 4.603585 3.748397 3.049954 3.307218 3.761928 3.223937 3.248733 2.450249 3.787715 3.689513 2.416641 3.688028 2.945025 2.814381 2.709609 2.441538 3.513737 3.055684 2.598900 2.948706 2.307710 2.853698 2.985202 3.023828 3.548635 3.598232 3.032659 4.321358 4.140341 3.474522 3.357744 3.813894 3.038143 3.207957 2.625004 2.996249 3.090329 2.886039 3.034749 3.325125 4.031631 4.489079 3.205908 3.745473 2.483872 3.089552 3.106395 2.924072 3.227552 2.392873 2.517460 4.668438 3.395064 2.584331 4.603495 3.628604 3.697639 3.664285 2.639885 4.302004 3.344648 2.971786 3.670431 4.592497 3.033464 2.430398 3.106837 3.441287 4.193225 2.934499 2.819149 3.681522 3.144699 2.984212 3.281283 3.888342 3.564382 2.127105 3.588921 3.046690 2.742882 4.347221 3.455500 2.356790 3.191730 4.296086 3.299529 3.815538 2.796436 4.889902 3.145662 3.692300 4.378192 3.714003 3.047781 2.713910 3.003245 2.928652 3.872506 4.292969 4.124807 3.891013 2.938720 3.037546 3.401142 2.185825 2.901349 3.395728 4.265339 4.029071 4.541066 2.652730 3.550852 3.324303 4.134499 3.424555 2.888292 3.760106 2.668945 3.830832 4.258113 3.113643 3.433290 2.962085 2.929725 2.766338 2.730298 3.284701 2.805025 3.931829 3.472464 3.490885 4.321940 3.384980 2.861355 3.323768 3.832758 4.792141 3.322715 4.429526 3.228657 2.738305 3.153571 2.620968 2.941511 3.300921 2.310268 4.838538 3.598101 3.964943 2.575188 2.804208 3.041037 3.723489 2.595606 2.655331 3.057019 4.053739 3.363386 2.811642 2.678791 3.050264 2.693815 3.506397 2.477411 5.028214 2.360215 4.541613 3.910832 3.764945 3.249834 2.868233 3.515980 3.041116 3.372765 2.772322 3.991572 3.292433 2.733358 3.020361 2.902275 2.446382 3.396496 4.306191 2.960804 2.356408 2.552303 3.231979 3.131169 3.301399 3.585957 4.063937 4.225351 3.486785 2.760649 2.561101 2.904716 3.642385 3.753307 3.637640 3.368807 2.468790 3.978436 4.150400 2.765817 3.978838 3.687359 2.781612 3.863692 3.643453 2.589056 3.869795 2.858958 2.569608 2.447623 4.093040 2.504063 4.010177 4.012614 3.089304 3.880934 4.177802 3.035029 3.851845 0
survival.upenn$YearsVisitToSample = survival.upenn$DaysVisitToSample/365

# Create survival object
survive_object = Surv(survival.upenn$YearsVisitToSample, survival.upenn$Event)

# Equally binning patients into quartiles based on their blood MIA concentration
# Convert the MIA factor variables into factor variables
survival.upenn$IGFBP.2_Factor <- with(survival.upenn, factor(
                            findInterval( IGFBP.2, c(-Inf,
                               quantile(IGFBP.2, probs=c(0.25,0.5,0.75)), Inf)), 
                            labels=c("First Quartile","Second Quartile","Third Quartile","Fourth Quartile")
      ))
survival.upenn$IGFBP.2_Factor = as.factor(survival.upenn$IGFBP.2_Factor)

IGFBP.2_Factor = coxph(survive_object ~ AgeAtSample + Sex + DieseaseDuration + IGFBP.2_Factor,data = survival.upenn)

# Take a look at the summary of Cox model
summary(IGFBP.2_Factor)
## Call:
## coxph(formula = survive_object ~ AgeAtSample + Sex + DieseaseDuration + 
##     IGFBP.2_Factor, data = survival.upenn)
## 
##   n= 71, number of events= 21 
## 
##                                   coef exp(coef) se(coef)     z Pr(>|z|)   
## AgeAtSample                   0.109164  1.115345 0.039163 2.787  0.00531 **
## SexMale                       1.329770  3.780176 0.563598 2.359  0.01830 * 
## DieseaseDuration              0.017114  1.017261 0.006306 2.714  0.00665 **
## IGFBP.2_FactorSecond Quartile 1.103468  3.014603 0.931204 1.185  0.23602   
## IGFBP.2_FactorThird Quartile  1.391837  4.022231 0.857409 1.623  0.10452   
## IGFBP.2_FactorFourth Quartile 1.991001  7.322860 0.841883 2.365  0.01803 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                               exp(coef) exp(-coef) lower .95 upper .95
## AgeAtSample                       1.115     0.8966    1.0329     1.204
## SexMale                           3.780     0.2645    1.2525    11.409
## DieseaseDuration                  1.017     0.9830    1.0048     1.030
## IGFBP.2_FactorSecond Quartile     3.015     0.3317    0.4859    18.701
## IGFBP.2_FactorThird Quartile      4.022     0.2486    0.7493    21.592
## IGFBP.2_FactorFourth Quartile     7.323     0.1366    1.4063    38.132
## 
## Concordance= 0.8  (se = 0.041 )
## Likelihood ratio test= 22.16  on 6 df,   p=0.001
## Wald test            = 16.04  on 6 df,   p=0.01
## Score (logrank) test = 20.77  on 6 df,   p=0.002
coxCurve_IGFBP.2 <- ggadjustedcurves(IGFBP.2_Factor, data = survival.upenn, method = "average", variable = "IGFBP.2_Factor", legend.title = "Plasma IGFBP.2 level Quartile") + 
  labs(title = "Clinical Conversion by IGFBP.2 subgroup \n (UPenn Cohort)", x = "Time (Years)", y = "% Cognitively Normal")  + 
  theme_classic() +
  theme(legend.position = c(0.35, 0.3), legend.title = element_text(face = "bold", size = 18), legend.text = element_text(size = 18),
        title = element_text(size = 18),
        axis.title.x = element_text(size = 20, margin = margin(t = 10, r = 0, b = 0, l = 0)),
        axis.title.y = element_text(size = 20, margin = margin(t = 0, r = 12, b = 0, l = 0)),
        axis.text = element_text(size = 20, color = "black"), axis.line = element_line(size = 1), axis.ticks = element_line(size = 1))
coxCurve_IGFBP.2

fit_coxph_MIAcog.forest <- ggforest(IGFBP.2_Factor , data = survival.upenn, main = "Hazard Ratios", 
                          cpositions = c(0.02, 0.23, 0.4), fontsize = 1, refLabel = "reference", noDigits = 2)
fit_coxph_MIAcog.forest

Longitudinal Motor Function Analaysis

UPDRS III (Motor Function) ~ Age + Sex + Disease Duration

setwd('/Users/junchaoshen/UPenn BMI')
UTSW.UPDRS = read.csv('UTSW UPDRS Longitudinal Final.csv')
UTSW.UPDRS.merge = merge(UTSW.UPDRS,df, by = 'GUID',all.x = TRUE)
kable(UTSW.UPDRS.merge[1:5,], "html") %>% kable_styling("striped") %>% scroll_box(width = "100%")
GUID YearstoVisit AgeAtSample AgeAtTest DiseaseDuration Sex UPDRS3 LEDD Group Site Age LDOPADoseTm MDS_UPDRS.Total.Score Gender PlateId CHIP CEBPB NSE PIAS4 IL.10.Ra STAT3 IRF1 c.Jun Mcl.1 OAS1 c.Myc SMAD3 SMAD2 IL.23 PDGFRA IL.12 STAT1 STAT6 LRRK2 Osteocalcin IL.5 GPDA IgA LPPL HEMK2 PDXK TLR4 REG4 HSP.27 YKL.40 Alpha.enolase Apo.L1 CD38 CD59 FABPL GDF.11 BTC HIF.1a S100A6 SECTM1 RSPO3 PSP Apoptosis.regulator.Bcl.W VEGF.D SOST FAM3D CSH EFNB1 SNP25 LYPD3 NEGR1 BCL6 FSTL1 Osteopontin Lumican CD177 CHKB SMOC1 protein.Z.inhibitor FLRT2 FLRT3 ISLR2 Vitronectin DSC2 LDLR HXK2 HXK1 SEM5A LTBP4 PIANP Adrenomedullin S100A4 RNF43 TRAIL.R4 ZNRF3 GI24 Ephrin.A2 ApoM IFN.b IFN10 IFNA7 EFNB2 HHLA2 IL.1.sRII AMGO2 RXFP1 C1QR1 NRG4 H2B2E H2A3 H31 IFN.g IL.1F8 IL.1F6 UCRP Ephrin.A3 X14.3.3.protein.beta.alpha X14.3.3E Annexin.V Myostatin C4b Coagulation.Factor.XI CTACK Endostatin TIMP.1 tPA EG.VEGF TIMP.2 TGF.b1 VEGF.sR3 C5 Apo.E BDNF C8 Cathepsin.G CXCL16..soluble FGF.10 FGF.8B GIIE GV MIP.3a SAP SCF.sR TIMP.3 Angiopoietin.4 Cadherin.E GFRa.3 Ephrin.B3 GFRa.2 X6Ckine HMG.1 OPG b.Endorphin Factor.I IGFBP.3 MCP.1 MMP.9 Myeloperoxidase PRL VEGF X4.1BB X4.1BB.ligand Angiopoietin.2 B7 CD30 CLF.1.CLC.Complex Cystatin.C Dtk eIF.5 Ephrin.A4 Ephrin.A5 ERBB2 ERBB3 ERBB4 GA733.1.protein gp130..soluble HO.2 HPV.E7.Type.16 HPV.E7.Type18 HSP.90a.b IL.1.R.AcP IL.10.Rb IL.12.Rb1 IL.13.Ra1 IL.2.sRg Layilin Lymphotoxin.b.R Macrophage.mannose.receptor M.CSF.R MSP.R PAFAH.beta.subunit P.Cadherin PKC.A PKC.Z Rab.GDP.dissociation.inhibitor.beta sICAM.3 suPAR TNF.sR.I TrkC BCMA Bone.proteoglycan.II Calpain.I CK.MM ERBB1 HGF HSP.60 iC3b IGFBP.5 IGFBP.6 MIA NPS.PLA2 OSM PECAM.1 Persephin PF.4 Protein.S TACI TECK Thyroxine.Binding.Globulin TNFSF18 CNTFR.alpha EMAP.2 EPO.R G.CSF.R IL.1F7 Laminin MICA NADPH.P450.Oxidoreductase NANOG NKp44 NovH Siglec.6 Siglec.7 Sonic.Hedgehog IgG TSLP.R ULBP.3 Activin.A Apo.A.I Azurocidin BMP.14 C1q C3 C3adesArg DRR1 FGF.18 FGF.19 FGF.20 FGF9 GDF.11.8 Hemopexin HIV.2.Rev I.309 IGFBP.1 IL.10 IL.16 IL.17F IL.22 Lactoferrin LAG.1 LD78.beta MCP.2 MMP.3 MMP.7 NAP.2 SOD Fibrinogen Apo.B ACE2 Activin.RIB ADAMTS.4 Angiopoietin.1 ART BCAM Cadherin.5 CD97 COMMD7 EDA Fractalkine.CX3CL.1 HAI.1 IL.27 Kallikrein.11 Kallikrein.4 kallikrein.8 Ku70 Lipocalin.2 Met MMP.17 OX40.Ligand sFRP.3 SPINT2 sTie.1 Ubiquitin.1 WIF.1 AIF1 C5a CHK1 ERK.1 Glucocorticoid.receptor Hat1 HDAC8 Karyopherin.a2 MEK1 MOZ PKC.D RAC1 RAD51 TBP Topoisomerase.I UBC9 YES a1.Antichymotrypsin C7 Cardiotrophin.1 CCL28 CD22 HCC.1 IL.4 Midkine MPIF.1 PCNA sRANKL PAI.1 Apo.E3 Apo.E4 Artemin Cytochrome.c Cytochrome.P450.3A4 DAN ER Factor.D Growth.hormone.receptor GX IGFBP.4 IGF.I Luteinizing.hormone Properdin Protein.C PTHrP SCGF.beta VCAM.1 TNFSF15 ALK.1 AREG BMP.7 CD36.ANTIGEN contactin.1 CTGF Desmoglein.1 EDAR ENA.78 ESAM Galectin.4 Gro.a Gro.b.g Histone.H1.2 ICOS IL.1.sRI IL.17.sR IL.18.Rb IL.1Rrp2 JAM.B JAM.C LSAMP MBL NKp30 PD.L2 PTP.1B Siglec.9 TGF.b.R.III TSLP CTLA.4 a2.Antiplasmin bFGF Calpastatin Ck.b.8.1 DC.SIGN DC.SIGNR FSH Galectin.2 GFAP IL.19 IL.1b I.TAC MIP.1a MRC2 Myoglobin ON PARC PTN resistin Trypsin vWF Fas.ligand..soluble Flt3.ligand Haptoglobin..Mixed.Type IL.4.sR NKG2D WISP.1 BAFF C9 Cathepsin.B FGF.5 Galectin.3 GDF.9 IgM IL.2 IL.13 IL.18.BPa LBP Coagulation.Factor.Xa PlGF TIG2 ULBP.1 ULBP.2 XEDAR Aurora.kinase.A MK01 SMAC VEGF.C sCD4 IL.2.sRa TNF.sR.II Siglec.3 ADAMTS.5 IDUA AMPM2 amyloid.precursor.protein ARSB ASAHL ATS1 ATS13 Carbonic.Anhydrase.IV CATC Cathepsin.A Cathepsin.S CD39 Coagulation.Factor.VII C2 CRIS3 Enterokinase WFKN1 Glutamate.carboxypeptidase GPVI Granulysin HPLN1 IDE IDS kallikrein.12 kallikrein.13 kallikrein.5 KREM2 LKHA4 LYVE1 MATN3 MEPE METAP1 ASAH2 Nidogen PIGR Protease.nexin.I RET SARP.2 Semaphorin.3A TrATPase URB WFKN2 Aggrecan ANGL3 BGH3 BGN C1r Carbonic.Anhydrase.X CD109 CD23 CD48 CD5L CFC1 CNTN2 Contactin.4 Contactin.5 CYTF Cystatin.M DLL4 FCG2A FCG2B FCG3B FCGR1 FCN2 GFRa.1 GPC2 Heparin.cofactor.II HTRA2 IGFBP.7 IL24 LRIG3 LRP8 LY9 MATN2 Nectin.like.protein.2 NET4 PGRP.S RGMB RGM.C TFPI TSP2 TSP4 ABL1 Aminoacylase.1 Antithrombin.III AURKB BARK1 BMP.1 CAMK2A CAMK2B Carbonic.anhydrase.6 Carbonic.anhydrase.VII CDK2.cyclin.A CDK5.p35 CDK8.cyclin.C Chk2 CLC4K CRDL1 CSK Cathepsin.V Dkk.4 ECM1 FETUB Granzyme.H HCK IL.17.RD Kallikrein.7 KPCI LYNB PAK3 PAK7 PCI PIK3CA.PIK3R1 PK3CG PKB.a.b.g PLK.1 Renin SHP.2 STAB2 TBK1 TCPTP TPSB2 TPSG1 UFC1 Bcl.2 BFL1 BMX BSP BTK CAMK1D CAMK2D Carbonic.anhydrase.XIII CD30.Ligand CDK1.cyclin.B Chymase CSK21 EphA1 EPHA3 FN1.3 FN1.4 Flt.3 FSTL3 granzyme.A GSK.3.alpha.beta HIPK3 IL.15.Ra IL.18.Ra IL.8 IR Kallistatin Kallikrein.6 LCK LYN Periostin PDGF.Rb PGCB PRKACA RPS6KA3 sE.Selectin STK16 Survivin Thrombopoietin.Receptor Thrombospondin.1 TrkA TRY3 DUS3 XPNPEP1 Angiotensinogen b2.Microglobulin b.ECGF BLC Catalase CNTF FGF.17 GCP.2 IFN.aA IL.17B Integrin.a1b1 LEAP.1 Lymphotoxin.a1.b2 Lymphotoxin.a2.b1 MDC MIP.5 Proteinase.3 SDF.1 TAFI TARC TGF.b3 TSH Vasoactive.Intestinal.Peptide CD40.ligand..soluble DKK1 dopa.decarboxylase Adiponectin a1.Antitrypsin a2.HS.Glycoprotein Arylsulfatase.A BASI BMP10 Cadherin.6 CAMK1 Caspase.3 CATE Chitotriosidase.1 CHL1 CLC7A MASP3 Discoidin.domain.receptor.2 DKK3 DPP2 Endothelin.converting.enzyme.1 EphB4 FCN1 GNS HGFA IL22RA1 LGMN LY86 Marapsin MMEL2 MP2K2 MRCKB Nectin.like.protein.1 NID2 OBCAM OCAD1 OLR1 RAP SLAF5 SLIK1 Soggy.1 TEC TLR4.MD.2.complex VEGF.sR2 BMPER Cadherin.12 Calcineurin.B.a complement.factor.H.related.5 IGF.II.receptor kallikrein.14 Macrophage.scavenger.receptor MFRP Albumin a2.Macroglobulin ALT Angiostatin CK.MB p27Kip1 BNP.32 PYY Secretin TNR4 CSF.1 Activated.Protein.C COX.2 STX1a sTie.2 ADAM.9 ANGL4 Cadherin.2 Carbonic.anhydrase.9 Carbonic.anhydrase.III CK.BB Cystatin.S CYTD Endocan EphA5 FGF23 FGFR.2 FGFR.3 FGR FYN IL.12.RB2 KPCT MAPK2 MAPK5 MAPKAPK3 MATK MK08 PAK6 PDGF.CC pTEN PTK6 TLR2 UFM1 AIP Cyclophilin.A DLRB1 ETHE1 GAPDH..liver HSP.40 MDHC NACA Peroxiredoxin.1 PPAC PSA1 PSA6 RS7 RSK.like.protein.kinase SBDS SE6L2 SGTA TCTP TMA UB2L3 ARI3A CaMKK.alpha CDC37 DLC8 IMB1 ING1 Lamin.B1 LDH.H.1 MBD4 Mesothelin NAGK Phosphoglycerate.mutase.1 PLPP PSD7 SKP1 Sorting.nexin.4 UBE2N discoidin.domain.receptor.1 FGF.4 HSP.70 sRAGE BPI C6 Eotaxin.2 Factor.B FGF.6 Fibronectin FST Granzyme.B HB.EGF IgE IL.17D IL.17E IL.20 IL.6.sRa IL.7 IP.10 Lymphotactin MCP.4 Neurotrophin.3 Neurotrophin.5 PAPP.A PDGF.BB Plasmin Plasminogen Prekallikrein alpha.1.antichymotrypsin.complex P.Selectin Tenascin TGF.b2 Thrombin uPA Factor.H MMP.2 Transferrin Histone.H2A.z Thyroglobulin X14.3.3 X4EBP2 X6.Phosphogluconate.dehydrogenase Aflatoxin.B1.aldehyde.reductase AK1A1 AN32B Cofilin.1 DRG.1 EP15R ERAB FER HNRPQ IF4G2 IGF.I.sR IL.1.R4 LCMT1 LIN7B M2.PK MDM2 NCAM.L1 NDP.kinase.B NSF1C NUDC3 PA2G4 paraoxonase.1 PESC PFD5 PHI prostatic.binding.protein Protein.disulfide.isomerase PSA2 RAN RBM39 SNAA Sphingosine.kinase.1 Spondin.1 Thymidine.kinase transcription.factor.MLR1..isoform.CRA_b Transketolase Triosephosphate.isomerase XTP3A PTP.1C AMNLS CYTT BOC CLC1B SAA CRP sICAM.1 DAPK2 DYRK3 MIC.1 DHH FGF.12 FGF.16 FGF.8A IFN.lambda.1 IFN.lambda.2 MSP SLPI SP.D ADAM12 BCL2.like.1.protein CHST2 CHST6 Collectin.Kidney.1 ENPP7 ENTP3 ENTP5 FCRL3 GREM1 hnRNP.A.B LRRT1 LRRT3 MFGM PCSK7 PDPK1 Sialoadhesin SPARCL1 SPHK2 ST4S6 TGM3 Tropomyosin.2 Ubiquitin ZAP70 C1.Esterase.Inhibitor C3b C4 C5b..6.Complex FGF7 IL.3.Ra IL.5.Ra IL.11 Kininogen..HMW MMP.12 NCAM.120 PDGF.AA SCGF.alpha ATS15 BSSP4 BST1 CBX5 CDON Clusterin CONA1 CTAP.III DnaJ.homolog EMR2 FLRT1 Fucosyltransferase.3 FUT5 GP114 HDGR2 IL.34 KIRR3 KYNU Livin.B NXPH1 PLCG1 PLXC1 RSPO2 SH21A SLIK5 SORC2 PH IL.6 X3HIDH GM.CSF TNF.b X41 X17.beta.HSD.1 Apo.D IL.3 PPIB Protein.disulfide.isomerase.A3 TFF3 Afamin Olfactomedin.4 ASM3A FAM107B Gelsolin CBG Cytidylate.kinase C34.gp41.HIV.Fragment PERL CO8A1 ITI.heavy.chain.H4 TXD12 STRATIFIN sL.Selectin TRAIL.R1 Epithelial.cell.kinase G.CSF Glypican.3 IL.1a BMPR1A BMP.RII TrkB VEGF121 Angiogenin Coagulation.Factor.IX Coagulation.Factor.X GDF2 Insulin MCP.3 WNT7A ACTH Glucagon C3a Calcineurin Caspase.2 Coactosin.like.protein Coagulation.Factor.V D.dimer Endoglin Galectin.8 GIB Glutathione.S.transferase.Pi GOT1 HCC.4 HCG Hemoglobin IgD Integrin.aVb5 Lysozyme MIP.3b MIS MMP.1 MMP.13 SHBG Stanniocalcin.1 TF EPI X40S.ribosomal.protein.SA AGR2 annexin.I annexin.II ARMEL ARP19 ARTS1 ATP.synthase.beta.chain C1QBP CAPG Carbonic.anhydrase.I carbonic.anhydrase.II CATZ cIAP.2 CRK DBNL DERM DSC3 Elafin ERP29 Esterase.D FABPE FAK1 FCAR FGFR4 Fibrinogen.g.chain.dimer GP1BA GPC5 GRN GSTA3 hnRNP.K HPG. HRG IF4A3 JAK2 LG3BP Mammaglobin.2 MMP.14 MK11 MK12 MK13 MAPK14 Mn.SOD Moesin PBEF Myokinase..human NCC27 NCK1 PAFAH PARK7 Peroxiredoxin.5 Peroxiredoxin.6 PGP9.5 phosphoglycerate.kinase.1 PPase PUR8 Rb RS3 sCD163 SEPR SIRT2 SPTA2 SSRP1 Tropomyosin.1.alpha.chain Trypsin.2 TS TSG.6 B7.H1 B7.H2 CD226 CD244 CD83 CLM6 CRTAM DAF DcR3 EPHAA EPHB2 EphB6 GPNMB IL.1.sR9 IL.17B.R IL.20.Ra IL.22BP IL.23.R IL.7.Ra ILT.2 ILT.4 JAG1 JAG2 JAML1 KI2L4 KI3L2 KI3S1 KLRF1 LAG.3 LIMP.II MICB MO2R1 NKp46 Nogo.Receptor NOTC2 Notch.1 Notch.3 Nr.CAM NRX1B NRX3B OX2G Prolactin.Receptor RELT ROBO2 ROBO3 SEM6B Semaphorin.6A sICAM.5 SIG14 SLAF6 SREC.I SREC.II TAJ TCCR TGF.b.R.II TIMD3 TWEAKR UNC5H3 UNC5H4 PDE7A AMPK.a1b1g1 K.ras NMT1 PDE9A PPID PSME3 GCKR CK2.A1.B CK2.A2.B PDK1 KIF23 IMDH1 HMGR PCSK9 NR1D1 PPIE MP2K4 JNK2 AMPK.a2b2g1 cGMP.stimulated.PDE Cyclophilin.F DRAK2 IMDH2 PDE11 PDE1A PDE3A PDE4D PDE5A TAK1.TAB1 TYK2 ABL2 BCAR3 calreticulin GRB2.related.adapter.protein.2 MMP.16 RAC3 SHC1 VAV GHC2 Eotaxin Coagulation.Factor.IXab Elastase Apo.E2 Troponin.T Prothrombin EGFRvIII annexin.VI B7.2 calgranulin.B Caspase.10 CBPE CKAP2 CPNE1 Cyclin.B1 DLL1 GPC6 hnRNP.A2.B1 HVEM IL.1Ra Keratin.18 LIGHT MIF NLGNX OMD PIM1 PKB.beta Semaphorin.3E SET BAFF.Receptor BRF.1 Fas..soluble sLeptin.R DR6 CAD15 CD27 RANK SHPS1 FABP Troponin.I..skeletal..fast.twitch Troponin.I ANP ALCAM ASGR1 CNDP1 COLEC12 CYTN DEAD.box.protein.19B Ficolin.3 GAS1 GRB2.adapter.protein H6ST1 HSP.90b IL.17.RC MED.1 PKC.B.II PKC.G PSMA RANTES RASA1 RBP RGMA RS3A sICAM.2 SLAF7 SRCN1 Stress.induced.phosphoprotein.1 Testican.1 Testican.2 WNK3 RUXF Cathepsin.D EGF GITR bFGF.R TRAIL.R2 NRP1 AFP BID b.NGF C3d CD70 Cripto Epo GDNF IFN.g.R1 IL.9 LIF.sR NG36 Noggin S100A12 tau X14.3.3.protein.zeta.delta X3HAO aldolase.A ARGI1 BAD Dynactin.subunit.2 EF.1.beta eIF.4H eIF.5A.1 Gastrin.releasing.peptide HINT1 HSP70.protein.8 Nucleoside.diphosphate.kinase.A PEX5 PSME1 S100A7 WISP.3 Ferritin TNF.a TWEAK Tpo PTH Somatostatin.28 MP2K3 p53 PolyUbiquitin.K48 PolyUbiquitin.K63 NET1 CD47 ANK2 X14.3.3.protein.theta CREL1 Lectin..mannose.binding.2 LRP1B MYPC1 N.terminal.pro.BNP Tropomyosin.4 PACAP.27 ghrelin PACAP.38 a.Synuclein BMP.6 HGH SOD3 RSPO4 Cathepsin.H Activin.AB PSA IGFBP.2 RNase.H1 ROR1 CgA MMP.10 FBLN3 Leptin KEAP1 gpIIbIIIa Eotaxin.3 SUMO3 IL.17 CSRP3 MMP.8 PGM1 DSCAM MUC1 FAM3B NEUREGULIN.1 INGR2 sLRP1 IFN.a.b.R1 TFF1 HMGN1 MIG CD63 Trefoil.factor.2 Galectin.7 LEG9 UB2G2 Transgelin.2 ATPO Corticotropin.lipotropin QORL1 PEDF CATF FTCD UBP25 PLXB2
PDAG965NUZ 0 76 76 2 Male 6 20 PD UTSW 76.3 20 31 Male Set.2 1591.7 1181.1 24859.2 1403.1 1662.2 2469.4 1371.1 3487.8 1320.0 390.8 466.4 25208.5 28676.8 788.3 71680.7 275.9 15660.6 677.1 2365.5 1399.2 290.9 1051.4 7693.5 633.9 4204.6 1424.0 499.4 1513.2 10491.9 4375.2 57221.5 35136.7 685.0 1957.9 2871.6 6520.2 534.1 476.0 3090.9 1362.5 1319.3 317.1 654.6 484.1 39947.5 22586.6 4984.5 1960.1 1364.7 1145.2 5653.7 1660.0 30873.7 4875.8 1248.4 762.6 2919.2 2967.8 3240.2 16120.1 5356.1 6382.4 75466.9 3241.9 191.7 634.3 1212.6 6599.0 1816.0 4219.2 2109.5 1904.9 1225.9 965.6 428.2 702.1 1024 15825.7 531.2 352.3 256.3 14755.6 1691.0 10803.4 2669.9 1686.9 11676.7 713.4 12413.4 699.6 1839.9 782.3 525.6 434.3 1665 1878.2 30519.8 1283.1 1022.2 3918.6 215.9 1559.1 1357.7 51358.3 70.1 565.6 172.2 401.3 1948.8 6518.7 8224.9 41634.0 1493.0 3268.4 948.1 11759.7 233.8 774.0 310.3 347.0 144.3 36729.8 16656.7 1362.3 742.9 66564.3 837.4 925.7 5129.0 21426.9 5480.5 14337.0 809.4 42455.7 1690.8 829.4 8437.7 19629.8 1750.8 11962.7 312.1 350.1 118.6 577.7 969.7 477.5 2448.6 552.3 1345.0 1665.9 2968.4 432.0 298.5 166.4 474.1 6558.1 438.4 354.7 2332.8 10805.6 27569.5 1165.8 506.0 3071.7 4470.6 1563.5 5618.9 14019.2 199.3 540.3 7366.2 28630.5 72880.2 910.7 24148.3 642.3 11144.0 1143.5 5846.6 9902.5 3937.4 62630.3 527.3 24934.8 1212.8 32018.5 6127.1 2173.9 714.3 2804.0 3094.9 1982.0 1020.9 285.5 87348.2 2541.9 1176 1338.4 37511.4 644.9 8302.8 3755.0 382.5 951.5 2067.8 2886.5 273.7 2998.1 185.1 389.6 684.2 183.9 1127.4 1386.7 225088.3 1517 938.1 3460.8 16618.3 308.5 3474.0 48185.8 72494.5 181021.8 305.9 269.9 5088.5 375.2 461.3 801.4 994.4 171.2 2807.1 712.1 185.3 326.4 643.8 844.9 43968.8 427.4 1510.5 249.0 453.5 1802.9 18221.6 1259.4 188805.7 12107.6 553.1 301.4 171.1 921.5 1608.8 1802.4 11110.8 1828.2 4142.2 1491.2 2840.0 7616.2 707.5 1070.4 296.6 2836.3 254.7 22978.7 9370.2 933.2 371.1 2800.1 1838.8 7815.7 3285.7 2742.0 3331.8 7055.5 536.5 40683.4 262.3 371.5 2377.9 307.9 414.6 1418.0 4796.1 49466.2 2350.9 535.2 508.6 3160.1 1309.3 329542.6 1839.4 773.6 5456.3 956.3 6410.6 597.2 20624.3 2312.6 192.9 2541.2 1799.4 258273.0 257445.1 510.7 2507.0 24718.2 983.0 773.9 1080.2 711.2 783.8 5342.9 1096.1 847.8 108637.8 868.8 991.5 1869.9 9745.2 881.5 1343.5 4230.8 913.6 9335.7 322.4 2017.7 2455.6 1983.0 518.0 4777.6 677.1 8659.9 1073.7 805.5 690.6 9998.6 1008.1 326.7 661.4 8603.1 6301.7 1677.0 19673.9 504.4 3378.3 3797.7 14123.2 28690.8 423.5 116.7 1756.4 1568.2 4309.6 53610.1 2464.0 46849.4 767.7 502.7 3514.4 5320.0 2360.6 2655.1 742.8 3495.3 1232.2 38113.5 5671.8 1526.0 1506.2 4310.4 37104.0 489.7 461.2 36787.4 218.4 490.0 5023.2 1363.1 82103.8 1950.6 466.4 19539.3 593.0 3418.7 898.6 779.4 9255.1 92707.9 1991.6 312.7 2088.0 440.7 489.5 791.9 1141.1 13840.5 6225.3 1604.0 366.8 1041.3 10592.2 3717.6 501.3 1311.1 43942.4 28063.0 859.4 9425.7 400.0 2766.4 1970.8 626.4 11630.2 1071.2 1554.2 348 2697.4 258 242.3 793.1 2219.2 16515.6 1390.9 1199.4 2169.0 4049.8 4464.6 1423.9 181.8 3937.3 1045.0 368.5 536.6 229.5 7304.3 3048.2 7308.5 3007.8 2059.8 765 4099.4 1812.1 8078.3 2659.4 7201.2 470.9 412.2 41984.0 2427.3 3619.0 652.5 4998.0 1884.6 705.6 2831.9 3498.0 1196.4 4537.1 669.7 822.9 12259.1 435.7 2475.3 1370.1 1328.0 282.7 1114.3 950.2 159.0 3669.4 7624.8 51411.6 294.1 5651.3 2217.2 2077.2 10543.6 2351.4 844.0 457.6 2197.6 1099.3 38792.3 13916.1 310.8 137.7 3670.1 133119.1 745.5 12011.6 2976.7 2079.4 5228.5 4716.7 318.7 1020.0 1275.8 2122.9 382.9 180.9 4092 16296.9 1281.0 8574.0 24249.6 4601.4 240.1 649.6 538.9 1220.7 4043.1 86224.1 1088.6 788.4 98553.7 937.2 2767.2 8696.6 188.7 845.5 12164.1 863.8 484.6 351.6 2302.3 190.1 26436.1 1578.6 1139.2 321 3656.9 19102.7 2228.7 17038 6246.4 2019.3 762.7 592.8 758.1 4015.8 137.9 4646.5 84330.0 5011.1 6217.2 2201.8 23735.4 555.2 800.9 9534.6 767.9 8130.0 42594.8 2292.9 354.7 19149.5 6756.4 7873.4 1957.5 2795.3 2126.2 13577.0 1918.6 578.9 243.3 6530.5 3353.7 457.5 3902.2 7626.2 5564.2 1706.8 186.6 2298.1 16356.7 219.9 196.0 1179.0 148.0 164.0 2137.8 17545.3 99 1843.1 1323.4 4900.3 4706.2 5107.3 14298.1 1536.0 466.2 575.8 391.2 1392.2 38874.4 318.5 5537.6 1280.7 1810.8 1481.3 429.3 1471.6 657.8 5778.6 21619.4 422.5 2747.1 14980.5 829.7 5096.0 1511.4 7772.9 1729.0 19403.9 3327.2 2298.2 2780.7 1726.1 888.3 2831.8 5397.4 6920.5 1140.0 1206.0 1491.1 1635.2 3660.8 5810.2 1055.2 1646.5 893.2 9551.1 1672.2 3053.6 8949.0 3955.3 5576.8 511.7 858.7 7819.1 1450.5 28456.2 8281.9 895.5 2059.6 18869.5 10666.4 3809.9 20041.0 338.3 700.5 597.7 3016.1 197.0 788.4 1218.5 891.5 545.2 341.8 1753.9 13043.2 4242.7 1750.4 1146.8 1750.4 710.4 151.4 9594.0 1452.1 792.6 796.3 243.6 910.1 7175.9 25505.2 1576.0 8383.8 13404.6 636.0 2037.5 359.0 1844.4 20365.6 406.6 717.9 254.9 620.7 8071.7 14248.1 166090.2 3361.2 3238.0 121254.6 1314.1 27096.4 8689.0 2863.5 10680.5 604.4 1194.2 1001.0 284.2 27318.4 3490.7 5690.0 19178.2 1688.3 2217.9 858.2 530.7 656.5 1619.3 12143.3 31645.8 832.7 1464.8 487.3 396.1 1098.8 2038.5 5359.8 365.1 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499.6 757.9 888.3 636.3 4969.8 488.7 579.0 597.4 326.3 645.9 94.0 5753.0 474.3 766.9 316.5 4076.1 2441.6 274.5 11775.7 373.9 423.8 303.6 365.0 5250.0 1350.2 3581.7 1160.8 2625.3 4125.0 13458.1 40857.5 149.0 2245.7 2933 1020.1 496.2 4436.7 649.7 1150.6 784.1 33585.6 2245.7 653.4 5250.2 1002.3 4000.5 1257.1 724.5 532.3 752.1 386.0 1182.8 472.6 4238.7 10250.8 5210.1 3309.6 141.1 950.5 173.4 357.1 6561.4 192.1 1863.0 307.2 3018.4 68072.0 12790.0 1861.7 439.3 998.9 6156.6 2799.2 16736.3 2898.3 348.6 3865.2 960.4 100120.3 296.1 9447.3 586.1 582.0 6099.0 2359.7 1730.3 825.7 1300.3 666.9 7006.7 13503.9 3420.6 2451.9 106187.1 1626.2 4381.5 1406.8 6377.8 161.0 4290.9 161.9 14539.5 3288.2 5355.1 249.9 5935.6 6172.0 2726.9 625.3 268.9 299.3 608.9 60807.8 1238.5 620.7 21727.7 164 182.0 1285.6 5587.8 415.8 114517.4 960.7 7390.0 735.4 676.7 1008.1 459.6 2933.5 37558 2283.0 470.8 17469.2 7215.7 1215.4 677.6 534.9 413.8 353.5 1516.4 4442.5 36039.1 1982.6 1102.6 545.4 4423.0 953.5 6408.6 122.3 404.9 778.6 2348.1 303.9 1482.1 903.2 1586.6 743.1 141.8 241.3 5091.1 583.7 17557.2 555.4 254.5 21874.5 1598.1 6092.3 750.3 888.0 241.2 2666.5 2512.8 300.2 4249.6 11202.1 628.1 213.6 374.8 1123.4 229.7 299.0 287.4 6367.8 304.5 541.2 906.3 3453.0 1321.2 672.0 22681.1 704.9 15649.3 680.8 1029.9 2445.2 909.6 2655.8 3101.9 302.0 5629.3 12834.8 1163.8 23614.8 354.0 9450.3 243.8 878.3 1093.1 681.5 10749.4 136.5 3853.2 5763.3 329.8 260.3 912.6 3946.9 368.9 33093.8 183.5 392.4 9911.0 573.5 1575.8 920.2 2295.7 265 607.3 397.1 1693.7 70699.1 1198.9 10261.5 5288.0 28416.1 3735.3 2258.4 2746.0 625.4 1388.1 1085.3 28428.1 3454.4 2270.8 1482.6 1343.4 1809.7 364.9 2309.8 11646.3 23347.6 28257.9 636.2 14039.7 7082.2 269.6 265712.9 3123.1 146883.0 193.0 10186.9 996.2 3313.2 3483.3 1825.5 12022.8 4813.5 309.6 1855.4 1590.2 3100.1 459.6 5274.7 740.1 399.8 1175.8 668.1 15392.6 594.6 22265.5 2911.1 635.8 2117.8 1548.1 1752.3 2052.3 8751.6 3772.8 7456.2 1193.7 12674.3 35717.4 4348.2 468.6 1015.0 16774.8 1287.8 5812.9 3009.4 1341.4 5766.1 191.2 748.5 70387.6 2060.2 47959.1 811.3 61389.5 10750.5 401.3 439.1 42496.4 1169.8 222.3 15033.8 542.8 1164.9 37919.1 93439.6 8319.1 3325.1 563.1 1201.7 2699.9 610.6 1122.5 646.1 797.3 278.1 3152.9 596.4 1848.9 428.6 6102.4 184.1 411.7 903.5 339.6 522.0 185.9 3904.9 613.9 2793.1 1100.2 129.5 164162.3 4589.3 81351.5 935.8 10950.4 1161.4 617.2 19176.2 46386.3 255.6 1518.7 1566.9 677.7 253.0 1246.2 179.8 869.1 13060.6 385.8 789.3 240.2 2530.8 1231.7 2202.1 735.5 1160.9 5096.5 477.3 757.3 491.3 1151.9 3473.0 8744.1 27201.5 1961.1 11932.4 97435.5 192.9 1267.7 204.8 9657.3 16653.9 266.6 849.9 296.3 1077.4 800.6 406.0 1012.6 108.8 684.1 12383.9 548.9 2135.1 11116.1 380.8 984.9 948.3 3463.9 271.5 4425.8 1327.6 426.1 979.2 970.8 1594.3 162.6 392.7 3068.9 2262.0 3092.3 833.7 453.4 365.9 11557.3 614.8 1261.6 4799.3 5252.0 5389.9 629.5 1338.5 56533.7 1992.0 5103.9 12593.1 4576.5
UTSW.UPDRS.merge$NCAM.120.Tertile <- with(Longitudinal, factor(
                            findInterval( NCAM.120, c(-Inf,
                               quantile(NCAM.120, probs=c(0.33,0.66)), Inf)), 
                            labels=c("First Tertile","Second Tertile","Third Tertile")))

# Train LME model to see how fasat and slow progressors differ
UPDRS = lmer(UPDRS3 ~ NCAM.120.Tertile*YearstoVisit + AgeAtTest + Sex + as.numeric(DiseaseDuration)+ (1 | GUID), data=UTSW.UPDRS.merge, REML=F)

data(efc)
theme_set(theme_sjplot())

p5 = plot_model(UPDRS, type = "pred", terms = c("YearstoVisit", "NCAM.120.Tertile"),
           title = " Cognitive Change \n (UTSW + Penn State Cohort)",
           axis.title = c("YearstoVisit","Cognition(MoCA)"),
           axis.labels = NULL, legend.title = "NCAM.120 Tertile",
           wrap.title = 50, wrap.labels = 25, axis.lim = NULL, grid.breaks = NULL,
           ci.lvl = 0.9, se = NULL, colors = "Set2", order.terms = c(3,1,2)) + scale_y_continuous(limits = c(0, 30), breaks = c(0,10,20,30,40)) + theme_classic() +
  theme(legend.position = c(0.25, 0.2), legend.title = element_text(face = "bold", size = 16), legend.text = element_text(size = 20),
        title = element_text(size = 23),
        axis.title.x = element_text(size = 25, margin = margin(t = 10, r = 0, b = 0, l = 0)),
        axis.title.y = element_text(size = 25, margin = margin(t = 0, r = 12, b = 0, l = 0)),
        axis.text = element_text(size = 25, color = "black"), axis.line = element_line(size = 1), axis.ticks = element_line(size = 1))

p5